---
title: "SIWA REPORT E335"
output:
flexdashboard::flex_dashboard:
output_dir: docs
orientation: rows
vertical_layout: scroll
source_code: embed
css: estilo.css
mathjax: NULL
self_contained: FALSE
#runtime: shiny
#navbar:
# - { title: "About SIWA", href: "https://siwa.bio/" }
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r libraries, include=FALSE, cache=TRUE}
###load libraries panels tabs####
library(phyloseq) #
library(DESeq2) #
library(kableExtra) #
library(genefilter) #
library(microbiome) #
library(ggplot2) #
library(vegan) #
library(ggpubr) #
library(ggplot2) #
library(plyr)
library(multcompView)
#####Load libraries correlations####
library(ggplot2)
library(phyloseq)
library(stringr)
library(dplyr)
#####Load libraries LR####
library(tidyverse)
library(ape)
library(ggpubr)
####Load libraries categorical histo####
library(kableExtra)
library(plyr)
library("fantaxtic")
library(data.table)
####load libraries Ratios-histo boxplots####
library(multcompView)
library(reshape)
### OTRICAS
library(plotly)
library(plyr)
library(flexdashboard)
library(shiny)
library(DT)
library(stringr)
## FUNCTIONS
source("/Volumes/GoogleDrive/Mi unidad/SIWAproject/Methods-review/functions.R")
```
```{r All inputs, include=FALSE}
#open phyloseq object
#folder <- "/Users/mcadavid/Documents/Reports_review/E335_Maria/"
#ODLEPobj <- readRDS(paste0(folder, "phyloseqObject_April11.rds"))
#open data-table
## Data creada con create_full_file_for_correlations_to_analytics - Jupyter Notebook
#complete_sample_table <-read.table(paste0(folder, "performance_histo_ge_ratios_alphadiv_for_correlations.csv"),check.names = FALSE, header=T, sep="\t")
#EAFIT surveillance tables (same for all reports)
#species_taxonomy_info <-read.csv(paste0(folder, "species_metabolic_effects.csv"),check.names = FALSE, sep=";")
#genera_taxonomy_info <- read.csv(paste0(folder, "genus_metabolic_effects.csv"), check.names = FALSE, sep=";")
#broad_taxonomy_info <- read.csv(paste0(folder, "broad_groups_metabolic_effects.csv"), check.names = FALSE, sep=";")
#open phyloseq object
ODLEPobj <- readRDS("/Users/mcadavid/Documents/Reports_review/E335_Maria/phyloseqObject_April11.rds")
#open data-table
## Data creada con create_full_file_for_correlations_to_analytics - Jupyter Notebook
complete_sample_table <-read.table("/Users/mcadavid/Documents/Reports_review/E335_Maria/Subset_exp1/Correlations/performance_histo_ge_ratios_alphadiv_for_correlations.csv",check.names = FALSE, header=T, sep="\t")
#EAFIT surveillance tables (same for all reports)
species_taxonomy_info <-read.csv(file="/Users/mcadavid/Documents/Reports_review/Metabolic_effects_table_V2/species_metabolic_effects.csv",check.names = FALSE, sep=";")
genera_taxonomy_info <- read.csv(file="/Users/mcadavid/Documents/Reports_review/Metabolic_effects_table_V2/genus_metabolic_effects.csv", check.names = FALSE, sep=";")
broad_taxonomy_info <- read.csv(file="/Users/mcadavid/Documents/Reports_review/Metabolic_effects_table_V2/broad_groups_metabolic_effects.csv", check.names = FALSE, sep=";")
#Deseq outputs
dfs_filtered <- readRDS(file="/Volumes/GoogleDrive/Mi unidad/SIWAproject/Reports/Version1.0/Input_data/dfs_filtered.RData")
```
Project description {data-icon="fa-table"}
=====================================
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### {data-width=200}
Project E335 was an experimental trial done at Iluma's broiler experimental farm located in the town of Fredonia, Antioquia (Colombia) at 1800m above sea level and an average annual temperature of 20º C. It aimed to evaluate the broilers' microbiome, gene expression and gut histology response to different Calcium and VitD levels in the broiler's diet.
The experiment consisted of 8 treatments differing in Ca concentration across the growth stages and also including presence or absence of VitD in the diet.
Microbiome, gut histology and gene expression data were collected from animals at 33 days of age, following treatment from day of hatch. For microbiome samples, gut content was collected from three different sampling locations: Cecum, Ileum and Feces. For gene expression and histology samples, tissue samples were collected from two sampling locations: Cecum and Ileum (refer to diagram).
### {data-with=800}
```{r picture, include= TRUE, echo=FALSE, out.width = '100%'}
knitr::include_graphics("diagram_E335.png")
```
# Exploration {data-navmenu="Microbiome"}
```{r Explore and filter Phyloseq Object, include=FALSE}
#Explore Phylosec object....
### OJO -->> REEMPLAZAR VitD NAME to send reports to externos
sample_data(ODLEPobj)$Treatment <- as.factor(stringr::str_replace(sample_data(ODLEPobj)$Treatment, "MediumAlphaD3","VitD"))
sample_data(ODLEPobj)$VitDLevel <- as.factor(sample_data(ODLEPobj)$Alphalevel)
sample_data(ODLEPobj)$VitDLevel_label <- as.factor(sample_data(ODLEPobj)$Alphad3level)
#Subset phyloseq object to include only wanted treatments
ODLEPobj <- subset_samples(ODLEPobj, TreatmentNumber%in%c("1","2","3","4","5","6","7","8"))
ODLEPobj <- subset_samples(ODLEPobj, SampleID != "0074_02C-M") #REMOVE OUTLIAR
metadata <- meta(ODLEPobj)
#Filter phyloseq object by location
ODLEPobj_cecum<-subset_samples(ODLEPobj, SampleLocation=="C")
ODLEPobj_ileum<-subset_samples(ODLEPobj, SampleLocation=="I")
ODLEPobj_feces<-subset_samples(ODLEPobj, SampleLocation=="F")
```
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### {data-width=150}
As seen in Table 1, six microbiome samples per treatment were collected in each gut location. Four samples are excluded or missing.
### TABLE 1: Samples per group {data-width=850}
```{r Table for number of samples per treatment, echo=FALSE, fig.width = 10}
cec = table(subset(as.data.frame(as.matrix(sample_data(ODLEPobj_cecum))), select = c("Treatment")))
ile = table(subset(as.data.frame(as.matrix(sample_data(ODLEPobj_ileum))), select = c( "Treatment")))
fec = table(subset(as.data.frame(as.matrix(sample_data(ODLEPobj_feces))), select = c("Treatment")))
tab=rbind(ile,cec,fec)
tab <- as.data.frame(t(tab))
colnames(tab)=c("Ileum","Cecum","Feces")
l <- list("LowCa",
"LowCa+VitD",
"MediumLowCa",
"MediumLowCa+VitD",
"MediumHighCa",
"MediumHighCa+VitD",
"HighCa",
"HighCa+VitD")
tab <- tab[order(match(rownames(tab), l)),]
ttab <-as.data.frame(t(tab))
kbl(ttab, centering = FALSE) %>%
kable_styling(full_width = F, position = "center") %>%
column_spec(2, color = spec_color(ttab$"LowCa", end = 0.5, direction= -1, option= "magma")) %>%
column_spec(3, color = spec_color(ttab$"LowCa+VitD",end = 0.5, direction= -1, option= "magma")) %>%
column_spec(4, color = spec_color(ttab$"MediumLowCa", end = 0.5, direction= -1, option= "magma")) %>%
column_spec(5, color = spec_color(ttab$"MediumLowCa+VitD", end = 0.5, direction= -1, option= "magma")) %>%
column_spec(6, color = spec_color(ttab$"MediumHighCa", end = 0.5, direction= -1, option= "magma")) %>%
column_spec(7, color = spec_color(ttab$"MediumHighCa+VitD", end = 0.5, direction= -1, option= "magma")) %>%
column_spec(8, color = spec_color(ttab$"HighCa", end = 0.5, direction= -1, option= "magma")) %>%
column_spec(9, color = spec_color(ttab$"HighCa+VitD", end = 0.5, direction= -1, option= "magma"))
```
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Sequences identified during the sequencing process are grouped in batches of identical sequences so that the number of times that sequences was found can be recorded. This is the abundance of the sequence. The groups of identical sequences are called Operational Taxonomic Units
(OTUs) or Amplicon Sequence Variants (ASVs), and they often (but not always) correspond to species or genera of bacteria. In the plot below we can observe the prevalence of OTUs across all samples (the fixed threshold for an OTU to be considered present in a sample is 0.1% relative abundance).
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### {data-width=300}
The sequencing process identified more than 1300 unique sequences, and as expected, only a few OTUs are shared by the majority of samples (Fig1). Black percentages in each graph (Fig1) indicate the percentage of OTUs in that site that are present in at least 50% of the samples. As seen in the graph, most are rare taxa detected in only a small portion of samples. It is common to find a small number of OTUs which are dominant in the community, while most others are much less abundant and they are unlikely to be biologically related to any performance or treatment-based effects.
### FIGURE 1:Prevalence of OTUs in samples {data-width=700}
```{r Prevalence Plot, fig.height = 4, fig.width= 5, echo=FALSE}
#OPTIONAL: OTUs of interest to highlight in the plot (add to the list the otu or otus of interest you want to highlight in the plot)
otus_of_interest= list()
#Ileum
pseq.rel_ileum <- microbiome::transform(ODLEPobj_ileum, "compositional")
otu_relative_ileum <- as.data.frame(otu_table(pseq.rel_ileum))
#colSums(otu_relative_ileum) #Check if normalized: sum relative abundance added should be one
#remove OTUs that are zero in all samples
otu_relative_ileum = otu_relative_ileum[!apply(otu_relative_ileum, 1, function(x) all(x == 0)), ]
total_samples= ncol(otu_relative_ileum)
total_otus= nrow(otu_relative_ileum)
#absent in how many samples?
absent=apply(otu_relative_ileum ==0, 1, sum) #podria cambiarlo por un threshold para considerar absent una OTU en una muestra <0.001
#add a col with the percentage of samples it is present
otu_relative_ileum$percentage_samples_present=(1-(absent/total_samples))*100
#add a col with OTU names
otu_relative_ileum$OTU <- row.names(otu_relative_ileum)
#add otus of interest to highlight in the plot
otu_relative_ileum$of_interest <- ifelse(otu_relative_ileum$OTU %in% otus_of_interest == TRUE, "YES", "NO")
#plot
colors <- c("#343aeb","#f56342")
sizes <- c(0.05, 4)
prev_plot_ileum=ggplot(data = otu_relative_ileum) +
theme_classic()+
aes(x = reorder(OTU,-percentage_samples_present,sum), y =percentage_samples_present, color=of_interest, size=of_interest) +
geom_point() +
scale_colour_manual(values=colors) +
scale_size_manual (values=sizes)+
#geom_point(color="#343aeb", size=0.05) +
ggtitle("Ileum") +
#xlab("OTU") + ylab("samples where OTU is present (%)")+
theme(legend.position = "none",
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank())
#Feces
pseq.rel_feces <- microbiome::transform(ODLEPobj_feces, "compositional")
otu_relative_feces <- as.data.frame(otu_table(pseq.rel_feces))
#colSums(otu_relative_feces) #Check if normalized: sum relative abundance added should be one
#remove OTUs that are zero in all samples
otu_relative_feces = otu_relative_feces[!apply(otu_relative_feces, 1, function(x) all(x == 0)), ]
total_samples= ncol(otu_relative_feces)
total_otus= nrow(otu_relative_feces)
#absent in how many samples?
absent=apply(otu_relative_feces ==0, 1, sum) #podria cambiarlo por un threshold para considerar absent una OTU en una muestra <0.001
#add a col with the percentage of samples it is present
otu_relative_feces$percentage_samples_present=(1-(absent/total_samples))*100
#add a col with OTU names
otu_relative_feces$OTU <- row.names(otu_relative_feces)
#add otus of interest to highlight in the plot
otu_relative_feces$of_interest <- ifelse(otu_relative_feces$OTU %in% otus_of_interest == TRUE, "YES", "NO")
#plot
colors <- c("#16DCC9","#f56342")
sizes <- c(0.05, 4)
prev_plot_feces=ggplot(data = otu_relative_feces) +
theme_classic()+
aes(x = reorder(OTU,-percentage_samples_present,sum), y =percentage_samples_present, color=of_interest, size=of_interest) +
geom_point() +
scale_colour_manual(values=colors) +
scale_size_manual (values=sizes)+
#geom_point(color="#16DCC9", size= 0.05) +
ggtitle("Feces") +
#xlab("OTU") + ylab("samples where OTU is present (%)")+
theme(legend.position = "none",
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank())
#Cecum
pseq.rel_cecum <- microbiome::transform(ODLEPobj_cecum, "compositional")
otu_relative_cecum <- as.data.frame(otu_table(pseq.rel_cecum))
#colSums(otu_relative_cecum) #Check if normalized: sum relative abundance added should be one
#remove OTUs that are zero in all samples
otu_relative_cecum = otu_relative_cecum[!apply(otu_relative_cecum, 1, function(x) all(x == 0)), ]
total_samples= ncol(otu_relative_cecum)
total_otus= nrow(otu_relative_cecum)
#absent in how many samples?
absent=apply(otu_relative_cecum ==0, 1, sum) #podria cambiarlo por un threshold para considerar absent una OTU en una muestra <0.001
#add a col with the percentage of samples it is present
otu_relative_cecum$percentage_samples_present=(1-(absent/total_samples))*100
#add a col with OTU names
otu_relative_cecum$OTU <- row.names(otu_relative_cecum)
#add otus of interest to highlight in the plot
otu_relative_cecum$of_interest <- ifelse(otu_relative_cecum$OTU %in% otus_of_interest == TRUE, "YES", "NO")
#plot
colors <- c("#BA4FC8","#f56342")
sizes <- c(0.05, 4)
prev_plot_cecum=ggplot(data = otu_relative_cecum) +
theme_classic()+
aes(x = reorder(OTU,-percentage_samples_present,sum), y =percentage_samples_present, color=of_interest, size=of_interest) +
geom_point() +
scale_colour_manual(values=colors) +
scale_size_manual (values=sizes)+
#geom_point(color="#BA4FC8", size=0.05) +
ggtitle("Cecum") +
#xlab("OTU") + ylab("samples where OTU is present (%)")+
theme(legend.position = "none",
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank())
## Add dased line to each plot
#ileum
otu_relative_ileum_more_50 = otu_relative_ileum[otu_relative_ileum$percentage_samples_present >= 50, c("OTU", "percentage_samples_present")]
more50_ileum = dim(otu_relative_ileum_more_50)[1]
prev_plot_ileum = prev_plot_ileum + geom_segment(aes(x=0, y=50, xend=more50_ileum, yend=50), linetype="dashed", color="black")
prev_plot_ileum = prev_plot_ileum + geom_segment(aes(x=more50_ileum, y=0, xend=more50_ileum, yend=50), linetype="dashed", color="black")
prev_plot_ileum= prev_plot_ileum + annotate("text", x = more50_ileum, y = 50, label = paste0(" ", round(more50_ileum/dim(otu_relative_ileum)[1] * 100, 1), "% OTUs"))
#cecum
otu_relative_cecum_more_50 = otu_relative_cecum[otu_relative_cecum$percentage_samples_present >= 50, c("OTU", "percentage_samples_present")]
more50_cecum = dim(otu_relative_cecum_more_50)[1]
prev_plot_cecum = prev_plot_cecum + geom_segment(aes(x=0, y=50, xend=more50_cecum, yend=50), linetype="dashed", color="black")
prev_plot_cecum = prev_plot_cecum + geom_segment(aes(x=more50_cecum, y=0, xend=more50_cecum, yend=50), linetype="dashed", color="black")
prev_plot_cecum= prev_plot_cecum + annotate("text", x = more50_cecum, y = 50, label = paste0(" ", round(more50_cecum/dim(otu_relative_cecum)[1] * 100, 1), "% OTUs"))
#feces
otu_relative_feces_more_50 = otu_relative_feces[otu_relative_feces$percentage_samples_present >= 50, c("OTU", "percentage_samples_present")]
more50_feces = dim(otu_relative_feces_more_50)[1]
prev_plot_feces = prev_plot_feces + geom_segment(aes(x=0, y=50, xend=more50_feces, yend=50), linetype="dashed", color="black")
prev_plot_feces = prev_plot_feces + geom_segment(aes(x=more50_feces, y=0, xend=more50_feces, yend=50), linetype="dashed", color="black")
prev_plot_feces= prev_plot_feces + annotate("text", x = more50_feces, y = 50, label = paste0(" ", round(more50_feces/dim(otu_relative_feces)[1] * 100, 1), "% OTUs"))
#Aggregted figure (3 locations prevalence graph)
figure <- ggarrange( prev_plot_cecum, prev_plot_ileum , prev_plot_feces,
ncol = 1, nrow = 3,
font.label = list(size = 10, color = "black", face = "plain"))
annotate_figure(figure,
bottom = text_grob("OTUs in order of prevalence"),
left = text_grob("Samples in which OTU is present (%)", rot=90))
```
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### {data-width=200}
After filtering low-abundance sequences, the remaining data can be explored more easily. We see below the distribution of these taxa at the phylum level.
### FIGURE 2: Prevalence of Major Phyla {data-width=800}
```{r Prevalence Phylum, echo=FALSE, include=TRUE, cache=TRUE}
pseq.rel <- microbiome::transform(ODLEPobj, "compositional")
flist <- filterfun(kOverA(5, 2e-05))
ODLEPobjRelFilter = filter_taxa(pseq.rel, flist, TRUE)
p <- plot_taxa_prevalence(ODLEPobjRelFilter, "Phylum")
p <- p + theme(legend.position='none')
ggplotly(p) %>%
layout(
xaxis = list(automargin=TRUE),
yaxis = list(automargin=TRUE)
) %>%
style(hoverinfo = 'none') %>% partial_bundle()
```
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### {data-width=200}
Next, we observe the distribution of taxa at the family level within each sample location. We can see that while the sample location has a strong impact on distribution, treatment does not affect the distribution of the families at first sight.
### FIGURE 3: Family distribution by sample locations {data-width=800}
```{r Family distribution, echo=FALSE}
#Family ditribution
ps.com.fam <- microbiomeutilities::aggregate_top_taxa2(ODLEPobjRelFilter,top = 8, "Family" )
ps_df <- microbiomeutilities::phy_to_ldf(ps.com.fam, transform.counts = "compositional")
plotfam <- ggstripchart(ps_df, "SampleLocation", "Abundance",
facet.by = "Family", color = "Treatment",
#size = 5,
ylab= "Relative abundances")
p<- ggpar(plotfam,legend = "right") #+ theme(text=element_text(size=25))
ggplotly(p) %>%
layout(
xaxis = list(automargin=TRUE),
yaxis = list(automargin=TRUE)
) %>%
style(hoverinfo = 'none') %>% partial_bundle()
```
# Taxonomic composition {data-navmenu="Microbiome"}
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In the following interactive graphs we can view the main genera present in the treatments at each location.
```{r Taxonomic composition , include=FALSE, warning=FALSE}
#####Plot Taxonomic composition feces#####
#Aggregate OTUs according to genus
ODLEPobj_cecum_glom <- tax_glom(ODLEPobj_cecum, taxrank="Genus", NArm=FALSE)
#create dataframe from phyloseq object
subset.genus.df <- psmelt(ODLEPobj_cecum_glom)
subset.genus.df$genus <- as.character(subset.genus.df$Genus) #convert to character
#calculate median rel. abundance
medians <- ddply(subset.genus.df, ~genus, function(x) c(median=median(x$Abundance)))
#calculate remainder
remainder <- medians[medians$median <= 0.01,]$genus
subset.genus.df[subset.genus.df$genus %in% remainder,]$genus <- "Genera < 1% abund."
#Figures abundances
subset.genus.df <- subset.genus.df %>%
mutate( Treatment=factor(Treatment,levels=c("LowCa", "LowCa+VitD", "MediumLowCa", "MediumLowCa+VitD", "MediumHighCa", "MediumHighCa+VitD", "HighCa","HighCa+VitD")) )
#plot with condensed genera into "< 1% abund" category by Treatment
plot_abundance_cecum<- ggplot(subset.genus.df, aes(x = Treatment, y = Abundance, fill = genus)) +
geom_bar(stat="identity", position="fill") +
xlab("Treatment") +
ylab("Relative abundance")+
#stat_compare_means(method = "anova")+
theme(axis.text.x = element_text(angle = 60, hjust = 1))+
theme(text=element_text(size=12))
#####Plot Taxonomic composition ileum#####
#Aggregate OTUs according to genus
ODLEPobj_ileum_glom = tax_glom(ODLEPobj_ileum, taxrank="Genus", NArm=FALSE)
#create dataframe from phyloseq object
subset.genus.df <- psmelt(ODLEPobj_ileum_glom)
subset.genus.df$genus <- as.character(subset.genus.df$Genus) #convert to character
#calculate median rel. abundance
medians <- ddply(subset.genus.df, ~genus, function(x) c(median=median(x$Abundance)))
#calculate remainder
remainder <- medians[medians$median <= 0.01,]$genus
subset.genus.df[subset.genus.df$genus %in% remainder,]$genus <- "Genera < 1% abund."
#Figures abundances
subset.genus.df <- subset.genus.df %>%
mutate( Treatment=factor(Treatment,levels=c("LowCa", "LowCa+VitD", "MediumLowCa", "MediumLowCa+VitD", "MediumHighCa", "MediumHighCa+VitD", "HighCa","HighCa+VitD")) )
#plot with condensed genera into "< 1% abund" category by Treatment
p <- ggplot(data=subset.genus.df, aes(x=reorder(Treatment, KitID), y=Abundance ,fill=genus))
plot_abundance_ileum<- p + geom_bar(stat="identity", position="fill") +
xlab("Treatment") +
ylab("Relative abundance")+
#stat_compare_means(method = "anova")+
theme(axis.text.x = element_text(angle = 60, hjust = 1))+
theme(text=element_text(size=12))
#####Plot Taxonomic composition feces#####
#Aggregate OTUs according to genus
ODLEPobj_feces_glom = tax_glom(ODLEPobj_feces, taxrank="Genus", NArm=FALSE)
#create dataframe from phyloseq object
subset.genus.df <- psmelt(ODLEPobj_feces_glom)
subset.genus.df$genus <- as.character(subset.genus.df$Genus) #convert to character
#calculate median rel. abundance
medians <- ddply(subset.genus.df, ~genus, function(x) c(median=median(x$Abundance)))
#calculate remainder
remainder <- medians[medians$median <= 0.01,]$genus
subset.genus.df[subset.genus.df$genus %in% remainder,]$genus <- "Genera < 1% abund."
#Figures abundances
subset.genus.df <- subset.genus.df %>%
mutate( Treatment=factor(Treatment,levels=c("LowCa", "LowCa+VitD", "MediumLowCa", "MediumLowCa+VitD", "MediumHighCa", "MediumHighCa+VitD", "HighCa","HighCa+VitD")) )
#plot with condensed genera into "< 1% abund" category by Treatment
p <- ggplot(data=subset.genus.df, aes(x=Treatment, y=Abundance ,fill=genus))
plot_abundance_feces<- p + geom_bar(stat="identity", position="fill") +
xlab("Treatment") +
ylab("Relative abundance")+
#stat_compare_means(method = "anova")+
theme(axis.text.x = element_text(angle = 60, hjust = 1))+
theme(text=element_text(size=12))
```
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### FIGURE 4A: Taxonomic composition CECUM {data-width=1000}
```{r, echo=FALSE, include=TRUE}
ggplotly(plot_abundance_cecum) %>%
layout(xaxis = list(automargin = TRUE),
yaxis = list(automargin = TRUE)) %>%
style(hoverinfo = 'none') %>% partial_bundle()
#ggplotly(plot_abundance_cecum) %>% partial_bundle()
```
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### FIGURE 4B: Taxonomic composition ILEUM {data-width=1000}
```{r, echo=FALSE, include=TRUE}
ggplotly(plot_abundance_ileum) %>%
layout(xaxis = list(automargin = TRUE),
yaxis = list(automargin = TRUE)) %>%
style(hoverinfo = 'none') %>% partial_bundle()
```
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### FIGURE 4C: Taxonomic composition FECES {data-width=1000}
```{r, echo=FALSE, include=TRUE}
ggplotly(plot_abundance_feces) %>%
layout(xaxis = list(automargin = TRUE),
yaxis = list(automargin = TRUE)) %>%
style(hoverinfo = 'none') %>% partial_bundle()
```
# Diversity {data-navmenu="Microbiome"}
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**Microbiome diversity**
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Then, we went on to evaluate diversity in the microbiome samples. Microbiome diversity can be assessed through multiple ecological indices that can be divided into two kinds of measures, alpha and beta diversity. Alpha diversity measures the variability of species within a sample while beta diversity accounts for the differences in composition between samples.
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**Alpha diversity**
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### {data-width=350}
Alpha diversity analyses showed a higher diversity for cecum compared to the other gut locations, larger microbiome alpha diversity being usually associated to better health status. However, it is also normal for each region of the gut to have different ranges of richness/diversity. The cecal microbiome tends to be very rich, while the small intestines and feces usually have a smaller number of bacterial species.
Further alpha diversity analyses showed a few differences between treatment groups within the cecum samples and within the ileum samples. In the fecal samples there were no differences between treatments regardless of the index used. This is not uncommon; many changes in microbial diversity occur at the level of abundance, rather than richness. In other words, the types of bacteria present in a community may not change very much, but abundances of individual groups of bacteria can shift, generating changes in the ecosystem and representing the real differences between treatments. And this can not always be captured using alpha diversity calculations.
### FIGURE 5: Alpha diversity grouped by sample location {data-width=650}
```{r , echo=FALSE}
#alpha diversity comparing locations
pdiv <- plot_anova_diversity_edit(ODLEPobj, method = c("richness", "shannon"),
grouping_column = "SampleLocation", pValueCutoff=0.05) +
xlab(NULL) + ylab(NULL) + theme(legend.position = "none")
pdiv
```
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### FIGURE 6A: Cecum diversity
```{r , echo=FALSE}
#Alpha diversity with ANOVA separated by location
#Cecum
pc <-plot_anova_diversity_edit(ODLEPobj_cecum, method = c("richness", "shannon"),
grouping_column ="Treatment",
pValueCutoff=0.05)+ xlab(NULL)+ ylab(NULL)+
theme(axis.text.x=element_blank(), axis.ticks.x=element_blank()) +theme(legend.position = "bottom",
axis.title.y=element_text(angle = 0, vjust = .5, size = 12)) +
theme(text=element_text(size=10))
pc
```
### FIGURE 6B: Ileum diversity
```{r , echo=FALSE}
#ileum
pi <- plot_anova_diversity_edit(ODLEPobj_ileum, method = c("richness", "shannon"),
grouping_column ="Treatment",
pValueCutoff=0.05)+ xlab(NULL)+ ylab(NULL)+
theme(axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
theme(legend.position = "bottom",
axis.title.y=element_text(angle = 0, vjust = .5, size = 12)) +
theme(text=element_text(size=10))
#ggplotly(pi) %>% partial_bundle()
pi
```
### FIGURE 6C: Feces diversity
```{r , echo=FALSE}
#feces
pf <-plot_anova_diversity_edit(ODLEPobj_feces, method = c("richness", "shannon"),
grouping_column ="Treatment",
pValueCutoff=0.05)+ xlab(NULL)+ ylab(NULL)+
theme(axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
theme(legend.position = "bottom",
axis.title.y=element_text(angle = 0, vjust = .5, size = 12)) +
theme(text=element_text(size=10))
#ggplotly(pf) %>% partial_bundle()
pf
```
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**Beta diversity**
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### {data-width=350}
Hence, we went on to evaluate those differences in bacterial abundances between the samples, this is also known as beta diversity or "compositional" distance. These distances (In our case Bray Curtis dissimilarities) are often visualized with a method called principal coordinates analysis (PCoA). Each axis represents a combination of features (OTUs) that account for high amounts of variation between samples. The percentage of the differences for which this combination of features accounts is shown on the axis. Samples that are on opposite ends of an axis that accounts for 20% of variability are likely to be more different than samples that are on opposite ends of an axis that only accounts for 5% of the total variability.
These analyses evidenced clear differentiation between samples of different gut locations (pval=0.001), which was biologically expected since different locations of the gut have different dynamics, roles and environmental conditions.
```{r , include=FALSE}
bray.dist <- phyloseq::distance(ODLEPobj, method = "bray")
metadata <- as(sample_data(ODLEPobj), "data.frame")
out.bray <- ordinate(ODLEPobj, method = "MDS", distance = "bray")
beta.bray = plot_ordination(ODLEPobj, out.bray, color="SampleLocation" )
group_location <- c("Cecal","Fecal", "Ileum")
colores <- c("#BA4FC8","#16DCC9", "#343aeb")
plot.beta.bray <- beta.bray + geom_point(size=4, alpha=0.75, na.rm=T) + #sobreponer un punto mas grande
scale_colour_manual(values=colores, labels = group_location) + #cambiar colores y poner nombre a cada grupo en leyenda
theme(axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank())
```
### FIGURE 7:Dissimilarity between all samples (Bray distances) {data-width=650}
```{r , echo=FALSE}
plot.beta.bray
```
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Despite the fact that the differences are not always apparent in the PCoAs, statistical significant differences were also found between the treatments within each location, including significance by calcium level and by VitD level.
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**By Treatment:**
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```{r, include=FALSE}
#####Betadiverisity Bray Cecum#####
bray.dist <- phyloseq::distance(ODLEPobj_cecum, method = "bray")
metadata <- as(sample_data(ODLEPobj_cecum), "data.frame")
out.bray <- ordinate(ODLEPobj_cecum, method = "MDS", distance = "bray")
beta.bray <- plot_ordination(ODLEPobj_cecum, out.bray, color="Treatment", axes=c(1, 2))
plot.beta.bray.cecum <- beta.bray + geom_point(size=4, alpha=0.75, na.rm=T) +
labs(col= "Treatment") + theme(axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank(),
legend.position="bottom")
#####Betadiverisity Bray Ileum#####
bray.dist <- phyloseq::distance(ODLEPobj_ileum, method = "bray")
out.bray <- ordinate(ODLEPobj_ileum, method = "MDS", distance = "bray")
beta.bray = plot_ordination(ODLEPobj_ileum, out.bray, color="Treatment")
plot.beta.bray.ileum <- beta.bray + geom_point(size=4, alpha=0.75, na.rm=T) + #sobreponer un punto mas grande
labs(col= "Treatment") + #titulo de la grafica y de la leyenda
theme(axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank(), legend.position="bottom")
#####Betadiverisity Bray Feces#####
bray.dist <- phyloseq::distance(ODLEPobj_feces, method = "bray")
out.bray <- ordinate(ODLEPobj_feces, method = "MDS", distance = "bray")
beta.bray <- plot_ordination(ODLEPobj_feces, out.bray, color="Treatment")
plot.beta.bray.feces <- beta.bray + geom_point(size=4, alpha=0.75, na.rm=T) + #sobreponer un punto mas grande
labs(col= "Treatment") + #titulo de la grafica y de la leyenda
theme(axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank(), legend.position="bottom")
```
### FIGURE 8A: Beta div Cecum (Treatment)
```{r , echo=FALSE}
plot.beta.bray.cecum
```
### FIGURE 8B: Beta div Ileum (Treatment)
```{r , echo=FALSE}
plot.beta.bray.ileum
```
### FIGURE 8C:Beta div Feces (Treatment)
```{r , echo=FALSE}
plot.beta.bray.feces
```
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**By Calcium level:**
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```{r, include=FALSE}
#####Betadiverisity Bray Cecum#####
out.bray <- ordinate(ODLEPobj_cecum, method = "MDS", distance = "bray")
beta.bray = plot_ordination(ODLEPobj_cecum, out.bray, color="Calcium_level", axes=c(1, 2))
plot.beta.bray.cecum<-beta.bray + geom_point(size=4, alpha=0.75, na.rm=T) + #sobreponer un punto mas grande
labs(col= "Calcium_level") + #titulo de la grafica y de la leyenda
theme(axis.text.x = element_blank(), axis.ticks = element_blank(), legend.position="bottom")
#####Betadiverisity Bray Ileum#####
out.bray <- ordinate(ODLEPobj_ileum, method = "MDS", distance = "bray")
beta.bray<-plot_ordination(ODLEPobj_ileum, out.bray, color="Calcium_level")
plot.beta.bray.ileum<-beta.bray + geom_point(size=4, alpha=0.75, na.rm=T) + #sobreponer un punto mas grande
labs(col= "Calcium_level") + #titulo de la grafica y de la leyenda
theme(axis.text.x = element_blank(), axis.ticks = element_blank(), legend.position="bottom")
#####Betadiverisity Bray Feces#####
out.bray <- ordinate(ODLEPobj_feces, method = "MDS", distance = "bray")
beta.bray<-plot_ordination(ODLEPobj_feces, out.bray, color="Calcium_level")
plot.beta.bray.feces<-beta.bray + geom_point(size=4, alpha=0.75, na.rm=T) + #sobreponer un punto mas grande
labs( col= "Calcium_level") + #titulo de la grafica y de la leyenda
theme(axis.text.x = element_blank(), axis.ticks = element_blank(), legend.position="bottom")
```
### FIGURE 9A: Beta div Cecum (Calcium)
```{r , echo=FALSE}
ggplotly(plot.beta.bray.cecum)%>%
layout(xaxis = list(automargin = TRUE),
yaxis = list(automargin = TRUE)) %>%
style(hoverinfo = 'none') %>% partial_bundle()
```
### FIGURE 9B: Beta div Ileum (Calcium)
```{r , echo=FALSE}
ggplotly(plot.beta.bray.ileum) %>%
layout(xaxis = list(automargin = TRUE),
yaxis = list(automargin = TRUE)) %>%
style(hoverinfo = 'none') %>% partial_bundle()
```
### FIGURE 9C: Beta div Feces (Calcium)
```{r , echo=FALSE}
ggplotly(plot.beta.bray.feces) %>%
layout(xaxis = list(automargin = TRUE),
yaxis = list(automargin = TRUE)) %>%
style(hoverinfo = 'none') %>% partial_bundle()
```
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**By VitD level:**
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```{r , echo=FALSE}
#####Betadiverisity Bray Cecum#####
#Ordination beta
out.bray <- ordinate(ODLEPobj_cecum, method = "MDS", distance = "bray")
beta.bray <- plot_ordination(ODLEPobj_cecum, out.bray, color="VitDLevel_label", axes=c(1, 2))
plot.beta.bray.cecum = beta.bray + geom_point(size=4, alpha=0.75, na.rm=T) + #sobreponer un punto mas grande
labs(col= "VitDLevel") + #titulo de la grafica y de la leyenda
theme(axis.text.x = element_blank(), axis.ticks = element_blank(), legend.position="bottom")
#####Betadiverisity Bray Ileum#####
#Ordination beta
out.bray <- ordinate(ODLEPobj_ileum, method = "MDS", distance = "bray")
beta.bray = plot_ordination(ODLEPobj_ileum, out.bray, color="VitDLevel_label")
plot.beta.bray.ileum = beta.bray + geom_point(size=4, alpha=0.75, na.rm=T) + #sobreponer un punto mas grande
labs(col= "VitDLevel") + #titulo de la grafica y de la leyenda
theme(axis.text.x = element_blank(), axis.ticks = element_blank(), legend.position="bottom")
#plot.beta.bray.ileum
#####Betadiverisity Bray Feces#####
#Ordination beta
out.bray <- ordinate(ODLEPobj_feces, method = "MDS", distance = "bray")
beta.bray = plot_ordination(ODLEPobj_feces, out.bray, color="VitDLevel_label")
plot.beta.bray.feces = beta.bray + geom_point(size=4, alpha=0.75, na.rm=T) + #sobreponer un punto mas grande
labs(col= "VitDlevel") + #titulo de la grafica y de la leyenda
theme(axis.text.x = element_blank(), axis.ticks = element_blank(), legend.position="bottom")
#plot.beta.bray.feces
```
### FIGURE 10A: Beta div Cecum (VitD)
```{r , echo=FALSE}
plot.beta.bray.cecum
```
### FIGURE 10B: Beta div Ileum (VitD)
```{r , echo=FALSE}
plot.beta.bray.ileum
```
### FIGURE 10C: Beta div Feces (VitD)
```{r , echo=FALSE}
plot.beta.bray.feces
```
# Differential Abundance {data-navmenu="Microbiome"}
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**Microbiome Differential Abundance Analysis**
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After comparing the grouping of samples by composition distances evidencing general trends in composition between treatments, we went on to look at methods to identify more specific changes. Microbiome data can be filtered, merged, and subset to make specific comparisons between any two groups, and at any taxonomic level. This with the goal to identify features (i.e., species, OTUs, gene families, etc.) that differ in abundance between two groups of samples according to their treatment. (We here use DESeq method).
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**Comparisons between presence and absence of VitD:**
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Here we compare two groups: one supplemented with vitD and the other one without VitD supplementation, regardless of calcium level. In the following graphs we can see the features that are differentially abundant in the presence of VitD compared to the absence of VitD.
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Hint: when dots are to the left it means these groups are more abundant in presence of VitD. When points are to the right these groups are more prevalent in absence of VitD
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### FIGURE 11A: Cecum diff - VitD level {data-width=500}
```{r , echo=FALSE}
#Be sure of the comparisons of interest we want to include in the report to add the corresponding title
comparison= 1
Title= "Cecum 0 VitD vs 1 VitD"
p_cecum = plot_deseq_report(dfs_filtered, comparison, Title)
p_cecum
```
### FIGURE 11B: Feces diff - VitD level {data-width=500}
```{r , echo=FALSE}
comparison= 7
Title= "Feces 0 VitD vs 1 VitD"
p_feces = plot_deseq_report(dfs_filtered, comparison, Title)
p_feces
```
# Metabolic information of species and genera {data-navmenu="Microbiome"}
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The following is an interactive table to browse available information and traits of bacteria of interest for a better understanding of the possible implications of changes in the microbiome.
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The table includes a probiotic potential score that ranges between -6 and 6. A higher score indicates multiple positive traits (e.g. Use as prebiotic, antibiotic, anti-inflammatory, antioxidant) associated with the bacterial group, while negative scores indicate negative traits (e.g. inflammatory, pathogen, opportunistic_pathogen, causes diarrhea, causes respiratory disease).
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### TABLE 2: Species and Genus
```{r sp groups table , echo=FALSE}
genera_taxonomy_info$Species <- "All species"
df= rbind(species_taxonomy_info,genera_taxonomy_info)
df=df[c("Genus","Species","Key_Findings", "Probiotic_potential", "Takeaways")]
colnames(df)[3] <- "Key_Findings"
colnames(df)[4] <- "Probiotic_Potential"
colnames(df)[5] <- "Proven_Traits"
brks <- quantile(df$Probiotic_Potential, probs = seq(.05, .95, .05), na.rm = TRUE)
#clrs <- round(seq(255, 40, length.out = length(brks) + 1), 0) %>% {paste0("rgb(255,", ., ",", ., ")")}
ramp <- colorRampPalette(c("#cb766e", "#a3d291"))
clrs <- ramp(length(brks)+1)
DT::datatable(df %>% select(-Key_Findings),
rownames = FALSE
) %>% formatStyle(
'Probiotic_Potential',
backgroundColor = styleInterval(brks, clrs)
)
```
# Metabolic information of broad groups {data-navmenu="Microbiome"}
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The following is an interactive table to browse available information and traits of broad groups of bacteria of interest for a better understanding of the possible implications of changes in the microbiome.
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### TABLE 3: Broad groups
```{r broad groups table , echo=FALSE}
df_2=broad_taxonomy_info[c("Group_Name","Finner_Classification", "Key_Findings")]
colnames(df_2)[2] <- "Taxonomic_level"
colnames(df_2)[3] <- "Key_Findings"
DT::datatable(df_2, height = 10000,
rownames = FALSE, options = list(pageLength = 10)
)
```
# Histopathology {data-icon="fa-table"}
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**Histopathology Analysis**
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We evaluated the physical structures of the intestinal tract tissue to understand the impact of calcium levels on gastrointestinal inflammation and integrity. We employ a scoring system that allows for semi-quantitative analysis of the integrity and inflammatory status of the gut. A score of 0 indicates a normal, healthy gut with no appearance of damage or aberration. A score of 5 for a given metric indicates extreme damage or aberration in the traits being evaluated.
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The treatment with recommended levels of Ca (2) with VitD showed better gastrointestinal tissue health compared to the rest of the groups, particularly those with higher levels of calcium.
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**Traits evaluated**
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*InflammationSeverity*: An overview of changes in the architecture or integrity of the intestines due to inflammation. These changes can be chronic, subacute, and acute. Changes may include ballooning of the crypt, edema, and loss of crypt cell walls. One aspect of the score accounts for the extent of the damage across the various layers of tissue in the intestine.
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*LymphoidImmune*: Infiltration of immune cells into the mucosa or serosa. The presence of immune cells in the mucosa is normal, but most are in or near aggregations of lymphoid tissue called Gut associated Lymphoid Tissue (GALT). High levels of lymphoid cells throughout the mucosa or serosa indicate a subacute adaptive immune response to a recent or current immune challenge. Similarly, growth in the GALT regions can indicate a pronounced antigenic challenge.
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*MicrobialOrganisms*: Normal and abnormal infiltration or attachment of microorganisms into the mucosa. May include assessment of organism type (i.e. parasite, yeast, or bacteria). Some association of microbes to the apical surface is normal. Higher scores indicate infiltration of abnormal microbe types and/or excessive levels.
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*MucosalIntegrity*: Uniformity and consistency of the mucosa at the apical membrane, where absorption occurs. Aberrations in this category include micro-erosion of the microvilli on the apical surface, ulcers, necrosis, and loss of gut-associated lymphoid tissue (GALT). When severe, these changes can include the submucosa level, too.
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*OverallArchitecture*: Morphology and structure of the mucosa. Aberrations would include blunting of villi, loss of mucus-producing goblet cells, and hyperplasia, in which excessive growth of absorptive enterocytes occurs at the expense of other important cell types such as goblet cells and endocrine cells. These aberrations are reparative mechanisms used by the animal, and typically indicate a repeated stress or injury to the GI at this location.
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*AdditiveScore*: Addition of all scores from all animals by treatment group. This is a summary of the overall condition of the intestine. However, it should be viewed with caution, as two groups or animals may have similar additive scores, but very different scores in individual traits. This would mean that the two groups have different underlying pathologies.
```{r Histo,fig.width = 10, fig.height = 5, include=FALSE}
histo <- complete_sample_table
#change labels
#Add treatments and treatment numbers (1-8)
histo <- histo %>%
mutate(TreatmentNumber = if_else(KitID == 70, 1,
ifelse(KitID == 71, 2,
ifelse(KitID == 72, 3,
ifelse(KitID == 73, 4,
ifelse(KitID == 74, 5,
ifelse(KitID == 75, 6,
ifelse(KitID == 76, 7,8))))))))
histo <- histo %>%
mutate(Treatment = if_else(KitID == 70, "LowCa",
ifelse(KitID == 71, "LowCa+VitD",
ifelse(KitID == 72, "MediumLowCa",
ifelse(KitID == 73, "MediumLowCa+VitD",
ifelse(KitID == 74, "MediumHighCa",
ifelse(KitID == 75, "MediumHighCa+VitD",
ifelse(KitID == 76, "HighCa",
ifelse(KitID == 77, "HighCa+VitD","other")))))))))
histo <- histo %>%
mutate(Alphalevel = if_else(KitID == 70, "0 VitD",
ifelse(KitID == 71, "VitD",
ifelse(KitID == 72, "0 VitD",
ifelse(KitID == 73, "VitD",
ifelse(KitID == 74, "0 VitD",
ifelse(KitID == 75, "VitD",
ifelse(KitID == 76, "0 VitD","VitD"))))))))
#subset by location
histo_cecum <- histo[histo$SampleLocation == 'C',]
histo_ileum <- histo[histo$SampleLocation == 'I',]
####Anova tests
#cecum AdditiveScore
res.aov <- aov(AdditiveScore ~ Treatment, data = histo_cecum)
summary(res.aov) #Additive score changes significantly with treatment in cecum. pval=0.00232*
#plot(res.aov, 1) # test 1. Homogeneity of variances
#plot(res.aov, 2) # test 2. Normality
tukey <- TukeyHSD(res.aov)
print(tukey)
#letters to add statistics to graph
cld <- multcompLetters4(res.aov, tukey)
cld2 <- data.frame(letters = cld$'Treatment'$Letters) #table with letters per treatment
cld2$Treatment <- rownames(cld2)
names(cld2)[1] <- "histosig"
#ileum AdditiveScore
res.aov <- aov(AdditiveScore ~ Treatment, data = histo_ileum)
summary(res.aov) #Additive score does not change significantly with treatment in ileum. pval=0.205
tukey <- TukeyHSD(res.aov)
print(tukey)
#letters to add statistics to graph
cld <- multcompLetters4(res.aov, tukey)
cld3 <- data.frame(letters = cld$'Treatment'$Letters) #table with letters per treatment
cld3$Treatment <- rownames(cld3)
names(cld3)[1] <- "histosig"
#pivot table to plot
histo_cecum2 <- histo_cecum %>%
pivot_longer(cols = c("OverallArchitecture", "MucosalIntegrity", "LymphoidImmune", "InflammationSeverity", "MicrobialOrganisms"), names_to = "ScoreCategory", values_to = "Value")
histo_ileum2 <- histo_ileum %>%
pivot_longer(cols = c("OverallArchitecture", "MucosalIntegrity", "LymphoidImmune", "InflammationSeverity", "MicrobialOrganisms"), names_to = "ScoreCategory", values_to = "Value")
```
```{r Histo plots by sample,fig.width = 10, fig.height = 5, include=FALSE}
#Stacked bars by SAMPLE
histo_by_samples_cecum <- ggplot(histo_cecum2, aes(fill = ScoreCategory, y =Value , x = SampleID)) +
geom_bar(stat = "identity") +
ggtitle("Histopathology scores by sample (cecum)") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
histo_by_samples_cecum
histo_by_samples_ileum <- ggplot(histo_ileum2, aes(fill = ScoreCategory, y =Value , x = SampleID)) +
geom_bar(stat = "identity") +
ggtitle("Histopathology scores by sample (ileum)") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
histo_by_samples_ileum
```
Row
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### FIGURE 12: Histopathology scores agreggated by Treatment - Cecum
```{r Histo plots by treatment-cec,fig.width = 10, fig.height = 8, echo=FALSE}
#Satcked bars by TREATMENT
#cecum
histo_c <- split(histo_cecum2, histo_cecum2$Treatment)
dff <- lapply(histo_c, function(x) ddply(x, .(ScoreCategory),
summarize, mean=mean(Value)))
listDF <- list()
for (i in 1:length(dff)){
#print(i)
dff[[i]]$Treatment <- names(dff)[i]
listDF[[i]] <- dff[[i]]
}
dfff <- do.call(rbind, listDF)
dfff <- dfff %>%
mutate( Treatment=factor(Treatment,levels=c("LowCa", "LowCa+VitD", "MediumLowCa", "MediumLowCa+VitD", "MediumHighCa", "MediumHighCa+VitD", "HighCa","HighCa+VitD")) )
treatments_cecum <- ggplot(dfff, aes(fill = ScoreCategory, y =mean , x =Treatment)) +
ylab("Mean Score") + xlab("") +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
theme(text=element_text(size=15))
#Add significancy letters manually
histo_cec_plot <- treatments_cecum+ annotate("text", x = 7, y = 13, label = cld2$histosig[2]) +
annotate("text", x = 6, y = 14, label = cld2$histosig[5]) +
annotate("text", x = 3, y = 14, label = cld2$histosig[3]) +
annotate("text", x = 8, y = 14, label = cld2$histosig[1]) +
annotate("text", x = 4, y = 14, label = cld2$histosig[8]) +
annotate("text", x = 5, y = 14, label = cld2$histosig[4]) +
annotate("text", x = 2, y = 14, label = cld2$histosig[6]) +
annotate("text", x = 1, y = 14, label = cld2$histosig[7])
ggplotly(histo_cec_plot)
```
Row
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### FIGURE 13: Histopathology scores agreggated by Treatment - Ileum
```{r Histo plots by treatment-ile,fig.width = 10, fig.height = 8, echo=FALSE}
#ileum
histo_i <- split(histo_ileum2, histo_ileum2$Treatment)
dff <- lapply(histo_i, function(x) ddply(x, .(ScoreCategory),
summarize, mean=mean(Value)))
listDF <- list()
for (i in 1:length(dff)){
#print(i)
dff[[i]]$Treatment <- names(dff)[i]
listDF[[i]] <- dff[[i]]
}
dfff <- do.call(rbind, listDF)
dfff <- dfff %>%
mutate( Treatment=factor(Treatment,levels=c("LowCa", "LowCa+VitD", "MediumLowCa", "MediumLowCa+VitD", "MediumHighCa", "MediumHighCa+VitD", "HighCa","HighCa+VitD")) )
treatments_ileum <- ggplot(dfff, aes(fill = ScoreCategory, y =mean , x = Treatment)) +
ylab("Mean Score") + xlab("") +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
theme(text=element_text(size=15))
#Add significancy letters manually
histo_cec_plot <- treatments_ileum+ annotate("text", x = 1:2, y = 7, label = cld3$histosig[1]) +
annotate("text", x = 5, y = 8, label = cld3$histosig[1]) +
annotate("text", x = 7, y = 8, label = cld3$histosig[1]) +
annotate("text", x = 3, y = 8, label = cld3$histosig[1]) +
annotate("text", x = 4, y = 8, label = cld3$histosig[1]) +
annotate("text", x = 6, y = 8, label = cld3$histosig[1]) +
annotate("text", x = 8, y = 8, label = cld3$histosig[1])
ggplotly(histo_cec_plot)
```
# Gene Expression {data-icon="fa-dna"}
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Row {data-height=30}
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**Gene Expression Analysis**
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The goal of gene expression is to evaluate how the animal host is responding to its environment by altering the levels of various proteins and other compounds in the body in a complex and very controlled manner. When studying gene expression with real-time polymerase chain reaction (PCR), scientists usually investigate changes (increases or decreases) in the expression of a particular gene or set of genes by measuring the abundance of the gene-specific transcript. Here we quantify expression for 3 genes related to inflammation and gut health, as a way to understand how the gut is responding to the gut microbiota and other stimuli. These target genes are: IL-10, IL-1B, and MUC2.
Row {data-height=100}
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To plot this data we use -log foldchange transformations relative to the control condition (in this case the control group is treatment "MediumLowCa" as this is the widly recommended level of calcium for broilers diet), which has all been normalized to your housekeeping gene. The higher the values, the higher the expression of the gene in the treatment compared to the control level. Significant differences between groups were tested by anova or multiple t-test (ie
Tukey’s test), resulting only in some group differences for the expression of IL10 gene in the cecum, and IL1B and IL10 genes in the ileum.
```{r Gene expression, include=FALSE}
GENE <- complete_sample_table
#Add treatments and treatment numbers (1-8)
GENE <- GENE %>%
mutate(Treatment = if_else(KitID == 70, "LowCa",
ifelse(KitID == 71, "LowCa+VitD",
ifelse(KitID == 72, "MediumLowCa",
ifelse(KitID == 73, "MediumLowCa+VitD",
ifelse(KitID == 74, "MediumHighCa",
ifelse(KitID == 75, "MediumHighCa+VitD",
ifelse(KitID == 76, "HighCa","HighCa+VitD"))))))))
GENE <- GENE %>%
mutate(TreatmentNumber = if_else(KitID == 70, 1,
ifelse(KitID == 71, 2,
ifelse(KitID == 72, 3,
ifelse(KitID == 73, 4,
ifelse(KitID == 74, 5,
ifelse(KitID == 75, 6,
ifelse(KitID == 76, 7,8))))))))
GENE <- GENE %>%
mutate(Alphalevel = if_else(KitID == 70, "0 VitD",
ifelse(KitID == 71, "VitD",
ifelse(KitID == 72, "0 VitD",
ifelse(KitID == 73, "VitD",
ifelse(KitID == 74, "0 VitD",
ifelse(KitID == 75, "VitD",
ifelse(KitID == 76, "0 VitD","VitD"))))))))
GENE$TreatmentNumber = as.factor(GENE$TreatmentNumber)
GENE$SampleLocation = as.factor(GENE$SampleLocation)
#Table:Samples by treatments
treatments = data.frame(table(GENE$TreatmentNumber))
colnames(treatments)=c("Treatment","Number of samples")
#Table:Samples by Samplelocation
location = data.frame(table(GENE$SampleLocation))
colnames(location)=c("Sample Location","Number of samples")
#Subset kits de interés y separar por location
subset <- GENE[GENE$TreatmentNumber %in% c(1,2,3,4,5,6,7,8),]
cec <- subset[subset$SampleLocation == "C",]
il <- subset[subset$SampleLocation == "I",]
#Calcular referencias (en este caso uso el tratamiento 3-MEDIUMLOWCA como mi control para el delta delta)
ref_value_IL10_i <- mean(il[il$TreatmentNumber == "3",]$DeltaCq_IL10)
ref_value_IL1B_i <- mean(il[il$TreatmentNumber == "3",]$DeltaCq_IL1B)
ref_value_MUC2_i <- mean(il[il$TreatmentNumber == "3",]$DeltaCq_MUC2)
ref_value_IL10_c <- mean(cec[cec$TreatmentNumber == "3",]$DeltaCq_IL10)
ref_value_IL1B_c <- mean(cec[cec$TreatmentNumber == "3",]$DeltaCq_IL1B)
ref_value_MUC2_c <- mean(cec[cec$TreatmentNumber == "3",]$DeltaCq_MUC2)
#Cecum calculations deltadelta and rq and new log transformation
cec$DD_IL1B<-cec$DeltaCq_IL1B - ref_value_IL1B_c
cec$RQ_IL1B<- (2^(-cec$DD_IL1B))
cec$RQ_IL1B_log<-(-log(cec$RQ_IL1B))
cec$DD_IL10<-cec$DeltaCq_IL10 - ref_value_IL10_c
cec$RQ_IL10<- 2^(-cec$DD_IL10)
cec$RQ_IL10_log<-(-log(cec$RQ_IL10))
cec$DD_MUC2<-cec$DeltaCq_MUC2 - ref_value_MUC2_c
cec$RQ_MUC2<- (2^(-cec$DD_MUC2))
cec$RQ_MUC2_log<-(-log(cec$RQ_MUC2))
#Ileum calculations deltadelta and rq and new log transformation
il$DD_IL1B<-il$DeltaCq_IL1B - ref_value_IL1B_i
il$RQ_IL1B<- (2^(-il$DD_IL1B))
il$RQ_IL1B_log<-(-log(il$RQ_IL1B))
il$DD_IL10<-il$DeltaCq_IL10 - ref_value_IL10_i
il$RQ_IL10<- 2^(-il$DD_IL10)
il$RQ_IL10_log<-(-log(il$RQ_IL10))
il$DD_MUC2<-il$DeltaCq_MUC2 - ref_value_MUC2_i
il$RQ_MUC2<- (2^(-il$DD_MUC2))
il$RQ_MUC2_log<-(-log(il$RQ_MUC2))
#Statistics for Deltacq values
#(note: when computing the anova with the logtransformed values we get the exact same pvalues)
#compute anova on treatment
res.aov1= aov(DeltaCq_IL1B ~ Treatment, data=cec)
res.aov2= aov(DeltaCq_IL10 ~ Treatment, data=cec)
res.aov3= aov(DeltaCq_MUC2 ~ Treatment, data=cec)
res.aov4= aov(DeltaCq_IL1B ~ Treatment, data=il)
res.aov5= aov(DeltaCq_IL10 ~ Treatment, data=il)
res.aov6= aov(DeltaCq_MUC2 ~ Treatment, data=il)
#summary anova on treatment
#summary(res.aov1) #pval= 0.025*
#summary(res.aov2) #pval= 0.009**
#summary(res.aov3) #pval= 0.787
#summary(res.aov4) #pval= 0.00192**
#summary(res.aov5) #pval= 0.123
#summary(res.aov6) #pval= 0.17
#tukey
tukey1 = TukeyHSD(res.aov1) #*
tukey2 = TukeyHSD(res.aov2) #**
tukey3 = TukeyHSD(res.aov3)
tukey4 = TukeyHSD(res.aov4) #**
tukey5 = TukeyHSD(res.aov5)
tukey6 = TukeyHSD(res.aov6)
#Define letters to add statistics to the graph
cld <- multcompLetters4(res.aov1, tukey1)
IL1B_cec <- data.frame(letters = cld$'Treatment'$Letters)
IL1B_cec$Treatment <- rownames(IL1B_cec)
names(IL1B_cec)[1] <- "IL1B_cec"
cld <- multcompLetters4(res.aov2, tukey2)
IL10_cec <- data.frame(letters = cld$'Treatment'$Letters)
IL10_cec$Treatment <- rownames(IL10_cec)
names(IL10_cec)[1] <- "IL10_cec"
cld <- multcompLetters4(res.aov3, tukey3)
MUC2_cec <- data.frame(letters = cld$'Treatment'$Letters)
MUC2_cec$Treatment <- rownames(MUC2_cec)
names(MUC2_cec)[1] <- "MUC2_cec"
cld <- multcompLetters4(res.aov4, tukey4)
IL1B_ile <- data.frame(letters = cld$'Treatment'$Letters)
IL1B_ile$Treatment <- rownames(IL1B_ile)
names(IL1B_ile)[1] <- "IL1B_ile"
cld <- multcompLetters4(res.aov5, tukey5)
IL10_ile <- data.frame(letters = cld$'Treatment'$Letters)
IL10_ile$Treatment <- rownames(IL10_ile)
names(IL10_ile)[1] <- "IL10_ile"
cld <- multcompLetters4(res.aov6, tukey6)
MUC2_ile <- data.frame(letters = cld$'Treatment'$Letters)
MUC2_ile$Treatment <- rownames(MUC2_ile)
names(MUC2_ile)[1] <- "MUC2_ile"
```
Row {data-height=500}
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### FIGURE 14A: Cecum - IL1B
```{r , echo=FALSE}
meanscec <- aggregate(RQ_IL1B_log ~ Treatment, cec, mean)
cec <- cec %>%
mutate( Treatment=factor(Treatment,levels=c("LowCa", "LowCa+VitD", "MediumLowCa", "MediumLowCa+VitD", "MediumHighCa", "MediumHighCa+VitD", "HighCa","HighCa+VitD")) )
plot1 <- ggplot(cec, aes(x = Treatment, y = round(RQ_IL1B_log), fill = Treatment)) +
geom_violin() + #geom_boxplot()+ #or geom_violin() +
ylab("Log-fold-change") + geom_hline(yintercept=0, linetype="dashed") +
theme(legend.position = "none") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))+
stat_summary(fun=mean, geom="point", size=2, color="red", fill="red")+
geom_text(data = meanscec, aes(label = round(RQ_IL1B_log, 2), y = RQ_IL1B_log + 0.2),
position = position_dodge(width = 1), vjust = -0.5, size = 4)+
coord_cartesian(ylim = c(-8, 8))+
geom_text(data = IL1B_cec, aes(x = Treatment, y = 6, label = IL1B_cec), size = 5)
plot1
```
### FIGURE 14B: Cecum - IL10
```{r fig14b, echo=FALSE}
meanscec <- aggregate(RQ_IL10_log ~ Treatment, cec, mean)
plot1 <- ggplot(cec, aes(x =Treatment, y = RQ_IL10_log, fill = Treatment)) +
geom_violin() + #geom_boxplot()+ #or geom_violin()
xlab("") +
ylab("Log-fold-change") + geom_hline(yintercept=0, linetype="dashed") +
theme(legend.position = "none") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))+
stat_summary(fun=mean, geom="point", size=2, color="red", fill="red")+
geom_text(data = meanscec, aes(label = round(RQ_IL10_log, 2), y = RQ_IL10_log + 0.2),
position = position_dodge(width = 1),vjust = -0.5, size = 4)+
coord_cartesian(ylim = c(-2.5, 1.5))+
geom_text(data = IL10_cec, aes(x = Treatment, y = 1, label = IL10_cec), size = 5)
plot1
```
### FIGURE 14C: Cecum - MUC2
```{r 14c, echo=FALSE}
meanscec <- aggregate(RQ_MUC2_log ~ Treatment, cec, mean)
plot1 <- ggplot(cec, aes(x = Treatment, y = RQ_MUC2_log, fill = Treatment)) +
geom_violin() + #geom_boxplot()+ #or geom_violin()
xlab("") +
ylab("Log-fold-change") + geom_hline(yintercept=0, linetype="dashed") +
theme(legend.position = "none") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))+
stat_summary(fun=mean, geom="point", size=2, color="red", fill="red")+
geom_text(data = meanscec, aes(label = round(RQ_MUC2_log,2), y = RQ_MUC2_log + 0.2),
position = position_dodge(width = 1),vjust = -0.5, size = 4)+
coord_cartesian(ylim = c(-6, 6))+
geom_text(data = MUC2_cec, aes(x = Treatment, y = 5, label = MUC2_cec), size = 5)
plot1
```
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### FIGURE 15A: Ileum - IL1B
```{r Gene expression plots ile il1b, echo=FALSE, warning=FALSE}
#ileum (plots with letters for statistics)
meansile <- aggregate(RQ_IL1B_log ~ Treatment, il, mean)
il <- il %>%
mutate( Treatment=factor(Treatment,levels=c("LowCa", "LowCa+VitD", "MediumLowCa", "MediumLowCa+VitD", "MediumHighCa", "MediumHighCa+VitD", "HighCa","HighCa+VitD")) )
plot1 <- ggplot(il, aes(x = Treatment, y = RQ_IL1B_log, fill = Treatment)) +
geom_violin() + #geom_boxplot()+ #or geom_violin()
xlab("") +
ylab("Log-fold-change") + geom_hline(yintercept=0, linetype="dashed") +
theme(legend.position = "none") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))+
stat_summary(fun=mean, geom="point", size=2, color="red", fill="red")+
geom_text(data = meansile, aes(label = round(RQ_IL1B_log,2), y = RQ_IL1B_log + 0.2),
position = position_dodge(width = 1),vjust = -0.5, size = 4)+
coord_cartesian(ylim = c(-5, 7)) +
geom_text(data = IL1B_ile, aes(x = Treatment, y = 7, label = IL1B_ile), size = 5)
plot1
```
### FIGURE 15B: Ileum - IL10
```{r Gene expression plots ile il10, echo=FALSE, warning=FALSE}
meansile <- aggregate(RQ_IL10_log ~ Treatment, il, mean)
plot1 <- ggplot(il, aes(x = Treatment, y = RQ_IL10_log, fill = Treatment)) +
geom_violin() + #geom_boxplot()+ #or geom_violin()
xlab("") +
ylab("Log-fold-change") + geom_hline(yintercept=0, linetype="dashed") +
theme(legend.position = "none") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))+
stat_summary(fun=mean, geom="point", size=2, color="red", fill="red")+
geom_text(data = meansile, aes(label = round(RQ_IL10_log,2), y = RQ_IL10_log + 0.2),
position = position_dodge(width = 1),vjust = -0.5, size = 4)+
coord_cartesian(ylim = c(-1, 1.6))+
geom_text(data = IL10_ile, aes(x = Treatment, y = 1.5, label = IL10_ile), size = 5)
plot1
```
### FIGURE 15C: Ileum - MUC2
```{r Gene expression plots ile muc2, echo=FALSE, warning=FALSE}
meansile <- aggregate(RQ_MUC2_log ~ Treatment, il, mean)
plot1 <- ggplot(il, aes(x =Treatment, y = RQ_MUC2_log, fill = Treatment)) +
geom_violin() + #geom_boxplot()+ #or geom_violin()
xlab("") +
ylab("Log-fold-change") + geom_hline(yintercept=0, linetype="dashed") +
theme(legend.position = "none") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))+
stat_summary(fun=mean, geom="point", size=2, color="red", fill="red")+
geom_text(data = meansile, aes(label = round(RQ_MUC2_log,2), y = RQ_MUC2_log + 0.2),
position = position_dodge(width = 1),vjust = -0.5, size = 4)+
coord_cartesian(ylim = c(-4, 2.3))+
geom_text(data = MUC2_ile, aes(x = Treatment, y = 2, label = MUC2_ile), size = 5)
plot1
```
# Microbiome + Gene expression{data-navmenu="Cross-panel Analyses"}
Row {data-height=50}
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Row {data-height=30}
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**Correlation microbiome + gene expression**
Row {data-height=50}
-----------------------------------------------------------------------
In addition to studying the role of treatments and challenges on SIWA metrics, we are also interested in understanding if there are associations/correlations between traits that might help us better understand the complex interactions in this system.
Row {data-height=100}
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We begin with Kendall correlations between the top 20 most abundant microbial taxa and the expression of IL-10, IL-1B, and MUC-2. A positive or red correlation indicates that when a taxon is increased, the expression of the correlated gene is also increased. The opposite it true when the correlation is negative (blue). It is important to understand that this correlation does not mean that changes in the microbiome *cause* changes in gene expression, or vice versa, only that they are moving in the same direction. The asterisk (*) indicates that the correlation is significant.
```{r open data 1, include=FALSE}
## Data creada con create_full_file_for_correlations_to_analytics - Jupyter Notebook
#meta with ratios for I and C
meta_exp <- complete_sample_table
#Extract taxonomy and otu table from phyloseq object
taxonomy <-as.data.frame(tax_table(ODLEPobj))
taxonomy$OTU <- row.names(taxonomy)
otu_table <- as.data.frame(otu_table(ODLEPobj))
otu_table$OTU<- row.names(otu_table)
#filter otus and taxonomy by the samples that are present in meta_exp (not needed)
#otu_table <- otu_table[c(meta_exp$SampleID, "OTU")]
#taxonomy <- filter(taxonomy, OTU %in% otu_table$OTU)
### AGGREGATE BY TAXA
otu_table_agg <- aggregate_otus_by_taxa(meta_exp, otu_table, taxonomy, "Family")
#R le pone una X y le quita el guion al sampleid - con check.names = FALSE se arregla. SINO, correr lo sgte:
#names(otu_table)<-sapply(str_remove_all(colnames(otu_table),"X"),"[")
#names(otu_table) <- str_replace_all(colnames(otu_table),'[.]', '-')
otu_table <- otu_table_agg ## para que a partir de aquí sea igual
#Transpose the data to have sample names on rows
otu_table <- as.matrix(otu_table)
otu_table <- t(otu_table)
class(otu_table)
rownames(otu_table) <-
sapply(str_remove_all(rownames(otu_table), "X"), "[") # porsi
#rownames(otu_table) #deben ser las samples
```
```{r transformation 1, include=FALSE}
#####transformation and filter for compositional data
rowz<-apply(otu_table == 0, 2, sum)/dim(otu_table)[1] #sum over columns (OTUS)
# dejar otus frecuentes, quitando los que están llenos de CEROS
p <- which(rowz>0.9)
otu_table_filtered <-otu_table[, -p]
# colz <- apply(otu_table_filtered == 0, 1, sum)/dim(otu_table_filtered)[2]
#pp<-which(colz>0.9)
otu_table_filtered<- as.matrix(otu_table_filtered)
require("zCompositions")
require("compositions")
otu_table_filtered_transformed <- cmultRepl(otu_table_filtered)
otu_table_filtered_transformed_log <- clr(otu_table_filtered_transformed)
dim(otu_table_filtered_transformed_log)
#Extract the corresponding meta_table for the samples in abund_table
rownames(meta_exp) <- meta_exp$SampleID
meta_exp <- meta_exp[rownames(otu_table_filtered_transformed_log), ]
rownames(otu_table_filtered_transformed_log)
colnames(meta_exp)
```
```{r choose variables 1, include=FALSE}
#When its ony one variable y just leave the c("") with onw variable.
#You can use sel_env to specify the variables you want to use and sel_env_label to specify the labels for the pannel
sel_vars <-
c(
"DeltaCq_IL10",
"DeltaCq_IL1B",
"DeltaCq_MUC2"
)
## Asi van a aparecer en el plot
sel_vars_label <- list(
"DeltaCq_IL10" = "DeltaCq_IL10",
"DeltaCq_IL1B" = "DeltaCq_IL1B",
"DeltaCq_MUC2" = "DeltaCq_MUC2"
)
sel_vars_label<-t(as.data.frame(sel_vars_label))
sel_vars_label<-as.data.frame(sel_vars_label)
colnames(sel_vars_label)<-c("Trans")
#Now get a filtered table based on sel_env --> DEJAR SOLO SAMPLES CON METADATOS
meta_exp_filtered<-meta_exp[,sel_vars]
meta_exp_filtered <- as.data.frame(meta_exp_filtered)
X <- otu_table_filtered_transformed_log
X <- X[, order(colSums(X), decreasing = TRUE)] #### REVISAAAAR CENTERED LOG RATIO TRANSFORM!!!!
dim(X)
#Extract list of top N Taxa
N<-20
otus_list<-colnames(X)[1:N]
X <- data.frame(X[, colnames(X) %in% otus_list], check.names = FALSE)
Y <- meta_exp_filtered
dim(X)
#Get grouping information
groups_ <- meta_exp$SampleLocation
#### Convertir las variables a numéricas
Y$DeltaCq_IL10 <- as.numeric(Y$DeltaCq_IL10)
Y$DeltaCq_IL1B <- as.numeric(Y$DeltaCq_IL1B)
Y$DeltaCq_MUC2 <- as.numeric(Y$DeltaCq_MUC2 )
```
```{r correlation method 1, include=FALSE}
#Choose correlation method
method<-"kendall"
#method <- "spearman"
#method <- "pearson"
#Now calculate the correlation between individual Taxa and the environmental data
df <- NULL
for (i in colnames(X)) {
for (j in colnames(Y)) {
for (k in c("C", "I")) {
a <- X[groups_ == k, i, drop = F]
b <- Y[groups_ == k, j, drop = F]
tmp <- c(
i,
j,
cor(a[complete.cases(b), ], b[complete.cases(b), ],
use = "everything", method = method),
cor.test(a[complete.cases(b), ], b[complete.cases(b), ], method =
method)$p.value,
k
)
if (is.null(df)) {
df <- tmp
}
else{
df <- rbind(df, tmp)
}
}
}
}
df<-data.frame(row.names=NULL,df)
colnames(df)<-c("Taxa","Env","Correlation","Pvalue","Type")
df$Pvalue<-as.numeric(as.character(df$Pvalue))
df$AdjPvalue<-rep(0,dim(df)[1])
df$Correlation<-as.numeric(as.character(df$Correlation))
adjustment_label<-c("NoAdj","AdjEnvAndType","AdjTaxaAndType","AdjTaxa","AdjEnv")
adjustment<-5
for(i in unique(df$Env)){
sel<-df$Env==i
df$AdjPvalue[sel]<-p.adjust(df$Pvalue[sel],method="BH")
}
#Now we generate the labels for signifant values
df$Significance <-
cut(
df$AdjPvalue,
breaks = c(-Inf, 0.01, 0.1, 0.2, Inf),
label = c("***", "**", "*", "")
)
df<-df[complete.cases(df),]
Env_labeller <- function(variable,value){
return(sel_vars_label[as.character(value),"Trans"])
}
df$label <- df$Taxa
```
Row
-----------------------------------------------------------------------
### FIGURE 16: Heatmap
```{r crear plot 1, include=TRUE,echo=FALSE, warning=FALSE, fig.dim = c(10, 6)}
p <- ggplot(aes(x = Type, y = Taxa, fill = Correlation), data = df)
p <-
p + geom_tile() + scale_fill_gradient2(low = "#2C7BB6", mid = "white", high =
"#D7191C")
p <-
p + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5))
p <-
p + geom_text(aes(label = Significance), color = "black", size = 3) + labs(y =
NULL, x = NULL, fill = method)
p <-
p + facet_grid(
. ~ Env,
drop = TRUE,
scale = "free",
space = "free_x",
labeller = Env_labeller
)+ theme(text=element_text(size=15))
p
```
# Microbiome + Histopathology {data-navmenu="Cross-panel Analyses"}
Row {data-height=50}
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Row {data-height=30}
-----------------------------------------------------------------------
**Taxonomic composition by histopathology score **
Row {data-height=80}
-----------------------------------------------------------------------
Histopathology scores are condensed from a 6-point scale (0-5) to a 3-point scale (mild, moderate, severe) in order to facilitate correlations between these scores and other traits of interest. However, due to the unbalanced nature of these scores (see tables below), it can be difficult to accurately assess correlations between these scores and other traits, and any significant correlations should be viewed very carefully.
```{r classify histo scores in categories, include=FALSE}
meta_exp <-complete_sample_table
#as character and round up
meta_exp$OverallArchitecture <- as.character(ceiling(meta_exp$OverallArchitecture))
meta_exp$MucosalIntegrity <- as.character(ceiling(meta_exp$MucosalIntegrity))
meta_exp$LymphoidImmune <- as.character(ceiling(meta_exp$LymphoidImmune))
meta_exp$InflammationSeverity <- as.character(ceiling(meta_exp$InflammationSeverity))
meta_exp$MicrobialOrganisms <- as.character(ceiling(meta_exp$MicrobialOrganisms))
#meta_exp <- meta_exp[complete.cases(meta_exp),] #keep only the ones with no missing values, im not using it because i want to keep all
#define levels
levels = c("0", "1", "2", "3", "4", "5")
meta_exp$OverallArchitecture <- factor(meta_exp$OverallArchitecture, levels=levels)
meta_exp$MucosalIntegrity <- factor(meta_exp$MucosalIntegrity, levels=levels)
meta_exp$LymphoidImmune <- factor(meta_exp$LymphoidImmune, levels=levels)
meta_exp$InflammationSeverity <- factor(meta_exp$InflammationSeverity, levels=levels)
meta_exp$MicrobialOrganisms <- factor(meta_exp$MicrobialOrganisms, levels=levels)
for (i in c("InflammationSeverity", "MicrobialOrganisms", "OverallArchitecture")){
print(i)
meta_exp[meta_exp[,i] %in% c("0","1"), paste0(i, "LEVEL")] <- "Mild"
meta_exp[meta_exp[,i] %in% c("2","3"), paste0(i, "LEVEL")] <- "Mod"
meta_exp[meta_exp[,i] %in% c("4","5"), paste0(i, "LEVEL")] <- "Sev"
}
meta_exp[meta_exp[,"LymphoidImmune"] %in% c("0","1", "2"), paste0("LymphoidImmune", "LEVEL")] <- "Mild"
meta_exp[meta_exp[,"LymphoidImmune"] %in% c("3","4"), paste0("LymphoidImmune", "LEVEL")] <- "Mod"
meta_exp[meta_exp[,"LymphoidImmune"] %in% c("5"), paste0("LymphoidImmune", "LEVEL")] <- "Sev"
meta_exp[meta_exp[,"MucosalIntegrity"] %in% c("0"), paste0("MucosalIntegrity", "LEVEL")] <- "Mild"
meta_exp[meta_exp[,"MucosalIntegrity"] %in% c("1","2"), paste0("MucosalIntegrity", "LEVEL")] <- "Mod"
meta_exp[meta_exp[,"MucosalIntegrity"] %in% c("3", "4", "5"), paste0("MucosalIntegrity", "LEVEL")] <- "Sev"
levels = c("Mild", "Mod", "Sev")
meta_exp$OverallArchitectureLEVEL <- factor(meta_exp$OverallArchitectureLEVEL, levels=levels)
meta_exp$MucosalIntegrityLEVEL <- factor(meta_exp$MucosalIntegrityLEVEL, levels=levels)
meta_exp$LymphoidImmuneLEVEL <- factor(meta_exp$LymphoidImmuneLEVEL, levels=levels)
meta_exp$InflammationSeverityLEVEL <- factor(meta_exp$InflammationSeverityLEVEL, levels=levels)
meta_exp$MicrobialOrganismsLEVEL <- factor(meta_exp$MicrobialOrganismsLEVEL, levels=levels)
#Mirar otra vez la distribucion de los puntajes de cada score pero ahora en categorias
histo <- meta_exp %>% dplyr::select(InflammationSeverityLEVEL, LymphoidImmuneLEVEL, MicrobialOrganismsLEVEL, MucosalIntegrityLEVEL, OverallArchitectureLEVEL, SampleLocation, SampleID)
#colnames(histo)
freq_c<- sapply(subset(histo[histo$SampleLocation == 'C', ], select = -c(SampleLocation,SampleID)), table)
table_sample_count_cecum <- as.data.frame(freq_c)
colnames(table_sample_count_cecum) <- str_replace(colnames(freq_c), "LEVEL", "")
freq_i<- sapply(subset(histo[histo$SampleLocation == 'I', ], select = -c(SampleLocation,SampleID)), table)
table_sample_count_ileum <- as.data.frame(freq_i)
colnames(table_sample_count_ileum) <- str_replace(colnames(freq_i), "LEVEL", "")
```
```{r Taxonomic composotion data open (phyloseq) + aggregate and add histo to phyloseq, include=FALSE}
rownames(histo) <- histo$SampleID
#colnames(histo)
ps <- merge_phyloseq(ODLEPobj, sample_data(histo))
#sample_data(ps)
ODLEPobj_rel <- microbiome::transform(ps, "compositional") #relative abundance
###aggregate taxa
taxonomy <-as.data.frame(tax_table(ODLEPobj_rel))
taxonomy <- dplyr::select(taxonomy,-c(SciName))
taxa_level <- "Genus"
phyloseq_ob_agg <-
microbiome::aggregate_taxa(ODLEPobj_rel, taxa_level)
phyloseq_ob_agg <-
phyloseq::prune_taxa(unique(taxonomy %>% pull(taxa_level)), phyloseq_ob_agg)
#Separate phyloseq object by location
phyloseq_ob_agg_cecum<-subset_samples(phyloseq_ob_agg, SampleLocation=="C")
phyloseq_ob_agg_ileum<-subset_samples(phyloseq_ob_agg, SampleLocation=="I")
```
```{r Create tables for plotings, include=FALSE}
phyloseq_ob_agg_cecum <- get_top_taxa(phyloseq_ob_agg_cecum, 15, relative = TRUE, discard_other = FALSE, other_label = "Other")
subset.genus.df_cecum <- psmelt(phyloseq_ob_agg_cecum)
phyloseq_ob_agg_ileum <- get_top_taxa(phyloseq_ob_agg_ileum, 15, relative=TRUE, discard_other=FALSE, other_label = "Other")
subset.genus.df_ileum <- psmelt(phyloseq_ob_agg_ileum)
categories <- list("OverallArchitectureLEVEL","MucosalIntegrityLEVEL", "LymphoidImmuneLEVEL","InflammationSeverityLEVEL","MicrobialOrganismsLEVEL")
categories_labels <- c(
"OverallArchitectureLEVEL"="Overall Architecture",
"MucosalIntegrityLEVEL"="Mucosal Integrity",
"LymphoidImmuneLEVEL"="Lymphoid Immune",
"InflammationSeverityLEVEL"= "Inflammation Severity",
"MicrobialOrganismsLEVEL" = "Microbial Organisms"
)
```
Row {data-height=200}
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### TABLE 4: Cecum- Sample count per category in each score
```{r imprimir cecum tablas con conteo de muestras, include=TRUE, echo=FALSE, warning=FALSE}
options(knitr.kable.NA = 0)
kbl(table_sample_count_cecum, row.names = TRUE) %>% kable_styling(position= "center", full_width = T)
```
Row {data-height=500}
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```{r prepare to plot 17, include=FALSE}
x <- subset.genus.df_cecum %>% dplyr::select(OTU,Abundance,
OverallArchitectureLEVEL,
MucosalIntegrityLEVEL,
LymphoidImmuneLEVEL,
InflammationSeverityLEVEL,
MicrobialOrganismsLEVEL)
Melted <- gather(
x,
key = "variable",
value = "level",
OverallArchitectureLEVEL,
MucosalIntegrityLEVEL,
LymphoidImmuneLEVEL,
InflammationSeverityLEVEL,
MicrobialOrganismsLEVEL
)
Melted$variable <- str_replace(Melted$variable, "LEVEL", "")
subset.genus.df_cecum %>%
gather(Ratio, Value, -SampleID)
```
### FIGURE 17: Cecum taxonomic composoition by histopathology score {data-width=400}
```{r 17, include=TRUE, echo=FALSE, warning=FALSE}
par(mar = c(6.5, 6.5, 0.5, 0.5), mgp = c(5, 1, 0))
p <- ggplot(data=Melted[!is.na(Melted["level"]),],
aes_string(x="level", y="Abundance",fill="OTU")) +
geom_bar(stat="identity", position="fill") +
theme(legend.position = "bottom") + facet_grid(. ~ variable) +
theme(axis.text.x = element_text(hjust = 1))
ggplotly(p) %>%
layout(
xaxis = list(automargin=TRUE), yaxis = list(automargin=TRUE)) %>% partial_bundle()
```
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Row {data-height=200}
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### TABLE 5: Ileum- Sample count per category in each score
```{r imprimir ileum tablas con conteo de muestras, include=TRUE, echo=FALSE, warning=FALSE}
options(knitr.kable.NA = 0)
kbl(table_sample_count_ileum, row.names = TRUE) %>% kable_styling(position= "left", full_width = T)
```
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```{r prepare to plot 18, include=FALSE}
y <- subset.genus.df_ileum %>% dplyr::select(OTU,Abundance,
OverallArchitectureLEVEL,
MucosalIntegrityLEVEL,
LymphoidImmuneLEVEL,
InflammationSeverityLEVEL,
MicrobialOrganismsLEVEL)
Melted <- gather(
y,
key = "variable",
value = "level",
OverallArchitectureLEVEL,
MucosalIntegrityLEVEL,
LymphoidImmuneLEVEL,
InflammationSeverityLEVEL,
MicrobialOrganismsLEVEL
)
Melted$variable <- str_replace(Melted$variable, "LEVEL", "")
subset.genus.df_cecum %>%
gather(Ratio, Value, -SampleID)
```
### FIGURE 18: Ileum taxonomic composoition by histopathology score {data-width=400}
```{r 18, include=TRUE, echo=FALSE, warning=FALSE}
par(mar = c(6.5, 6.5, 0.5, 0.5), mgp = c(5, 1, 0))
p <- ggplot(data=Melted[!is.na(Melted["level"]),],
aes_string(x="level", y="Abundance",fill="OTU")) +
geom_bar(stat="identity", position="fill") +
theme(legend.position = "bottom") + facet_grid(. ~ variable) +
theme(axis.text.x = element_text(hjust = 1))
ggplotly(p) %>%
layout(
xaxis = list(automargin=TRUE), yaxis = list(automargin=TRUE)) %>% partial_bundle()
```
# Gene expression + Histopathology {data-navmenu="Cross-panel Analyses"}
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Row {data-height=30}
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**Gene expression by histopathology score**
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Similar to the previous tab, these graphs explore the relationship between histopathology scores and another trait of interest, gene expression. Again, due to imbalances in the dataset, view any changes seen here with caution.
```{r ge histo organize, include=FALSE}
#####Open ge data#####
ge_data <-
subset(
complete_sample_table,
select = c("SampleID", "DeltaCq_IL1B" , "DeltaCq_IL10", "DeltaCq_MUC2")
)
ge_data_melted <- ge_data %>%
gather(Gene, Value,-SampleID)
####Organize histo data####
histo_data_class <-
subset(
histo,
select = c(
"OverallArchitectureLEVEL",
"MucosalIntegrityLEVEL",
"LymphoidImmuneLEVEL",
"InflammationSeverityLEVEL",
"MicrobialOrganismsLEVEL",
"SampleID"
)
)
histo_data_melted <- histo_data_class %>%
gather(Category, Score,-SampleID)
boxplot_data <-
merge(ge_data_melted, histo_data_melted , by = "SampleID")
boxplot_data$SampleLocation <- str_sub(boxplot_data$SampleID, -3,-3)
boxplot_data <- boxplot_data[!is.na(boxplot_data$Value),]
boxplot_data$Category <- str_replace(boxplot_data$Category, "LEVEL", "")
#Separate data for C and I
boxplot_data_cecum <- boxplot_data[boxplot_data$SampleLocation =="C",]
boxplot_data_ileum <- boxplot_data[boxplot_data$SampleLocation =="I",]
```
Row
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### FIGURE 19: Gene expression vs Histo - CECUM
```{r ge histo plots cec, include=TRUE, echo=FALSE, fig.dim = c(10, 6)}
#cecum plot
bp <- ggplot(boxplot_data_cecum, aes(x=Score, y=Value)) +
geom_boxplot(aes(fill=Category)) + facet_grid(Gene~ Category)+
xlab("") + ylab("Gene expression") +
theme(legend.position = "none")
bp
```
Row
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### FIGURE 20: Gene expression vs Histo - CECUM
```{r ge histo plots ile, include=TRUE, echo=FALSE, fig.dim = c(10, 6)}
#Ileum plot
bp <- ggplot(boxplot_data_ileum, aes(x=Score, y=Value)) +
geom_boxplot(aes(fill=Category)) + facet_grid(Gene~ Category)+
xlab("") + ylab("Gene expression") +
theme(legend.position = "none")
bp
```
# SIWA Ratios {data-icon="fa-table"}
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**SIWA Ratios and Indexes in relation to Histopathology and Gene Expression panels.**
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Below, you will find linear regressions between single traits (alpha diversity or log ratios of bacteria) and gene expression and histopathology. A regression score is calculated (R-squared), and a line is plotted to show the relationship between the traits. A slope from high to low indicates a negative relationship between the traits, while low-high indicates a positive relationship. The R-squared value suggests the size of the effect, and the tables below show the calculated p-values for each regression analysis.
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**Diversity indexes:**
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*Observed features* = represents richness in the sample, defined as the number of different species present in it.
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*Shannon diversity* = This index measures the homogeneity in abundance of the different species in a sample. In other words, shannon index is an estimate of how complex the community is, both the number of different bacteria, and also how different these bacteria are in their function or genetics.
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**SIWA Ratios:**
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These ratios each use two major categories of bacteria in an attempt to simplify the complex microbial community into a single value that can tell us something about the state of the community. These values should be interpreted carefully, as they ignore a lot of information and do not tell a complete story. However, they may be correlated with other variables of interest in a useful way.
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*SIWA Ratio 1*= Lactobacillus/ Escherichia-Shigella
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Lactobacillus species are overwhelmingly beneficial to the host, while E coli species include commensal strains as well as opportunistic and pathogenic strains. A comparison of the two summarizes the load of both beneficial and potentially pathogenic microbes in the small intestine. A higher ratio of Lactobacillus to Escherichia-Shigella would be expected in healthier animals.
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*SIWA Ratio 2*= Firmicutes/ Proteobacteria
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Firmicutes include both beneficial (Lactobacillus, Bacillus, Ruminococcus, Lachnospiraceae, and Pediococcus) and pathogenic (Clostridium, Streptococcus, Staphylococcus, and Listeria) groups. Proteobacteria include pathogenic groups such as E coli, Salmonella, Shigella, Legionella, Vibrio and Pseudomonas. While Firmicutes may contain pathogenic bacteria as well as beneficial ones, a higher ratio of Firmicutes:Proteobacteria would be expected to be associated with better health.
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*SIWA Ratio 3*= Lactobacillus/rest of the genera
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Lactobacillus can account for as much as 90% of sequenced bacteria in the ileum of chickens. By tracking this ratio, we can understand if higher or lower levels of Lactobacillus are correlated with health and performance outcomes.
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**Linear regressions between alpha diversity indexes and gene expression**
```{r data and functions, include=FALSE}
#####Open data#####
#####Separate by sample location#####
metadata_cecum <- complete_sample_table[complete_sample_table$SampleLocation=="C",]
metadata_ileum<- complete_sample_table[complete_sample_table$SampleLocation=="I",]
plot_regression <- function(x, y, metadata, x_label, y_label) {
Model <- lm(y ~ x, data = metadata)
R2= as.character(signif(summary(Model)$r.squared,digits=3))
R2= paste("R2=", R2)
plott=ggplot(metadata, aes(x=x, y=y)) +
geom_point() +
geom_smooth(formula = y ~ x, method=lm, se=FALSE)+
annotate("text", -Inf, Inf, label = R2 , hjust = 0, vjust = 1)+
xlab(x_label) + ylab(y_label)
return(plott)
}
####function to get R2####
get_stats_R <- function(x, y, metadata) {
Model <- lm(y ~ x, data = metadata)
statR= summary(Model)$r.squared
return(statR)
}
####function to get Pval of model####
get_stats_P <- function (x, y, metadata) {
modelobject<- lm(y ~ x, data = metadata)
if (class(modelobject) != "lm") stop("Not an object of class 'lm' ")
f <- summary(modelobject)$fstatistic
p <- pf(f[1],f[2],f[3],lower.tail=F)
attributes(p) <- NULL
return(p)
}
```
Row
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### FIGURE 21: Alpha diversity vs Gene expression - Cecum {data-width=500}
```{r aplhadiv + ge cecum, include=TRUE, echo=FALSE, warning=FALSE}
#Grid 1: CECUM
metadata= metadata_cecum
#IL10 + alphadiv
x_label= "Observed features"
y_label= "IL10"
x <- metadata$Alfa_Observed
y <- metadata$DeltaCq_IL10
cec_IL10_Alfa_Observed= plot_regression(x, y, metadata, x_label, y_label)
cec_IL10_Alfa_Observed_R=get_stats_R(x, y, metadata)
cec_IL10_Alfa_Observed_P=get_stats_P(x, y, metadata)
x_label= "Shannon diversity"
y_label= "IL10"
x <- metadata$Alfa_Shannon
y <- metadata$DeltaCq_IL10
cec_IL10_Alfa_Shannon= plot_regression(x, y, metadata, x_label, y_label)
cec_IL10_Alfa_Shannon_R=get_stats_R(x, y, metadata)
cec_IL10_Alfa_Shannon_P=get_stats_P(x, y, metadata)
#IL1B + alphadiv
x_label= "Observed features"
y_label= "IL1B"
x <- metadata$Alfa_Observed
y <- metadata$DeltaCq_IL1B
cec_IL1B_Alfa_Observed= plot_regression(x, y, metadata, x_label, y_label)
cec_IL1B_Alfa_Observed_R=get_stats_R(x, y, metadata)
cec_IL1B_Alfa_Observed_P=get_stats_P(x, y, metadata)
x_label= "Shannon diversity"
y_label= "IL1B"
x <- metadata$Alfa_Shannon
y <- metadata$DeltaCq_IL1B
cec_IL1B_Alfa_Shannon= plot_regression(x, y, metadata, x_label, y_label)
cec_IL1B_Alfa_Shannon_R=get_stats_R(x, y, metadata)
cec_IL1B_Alfa_Shannon_P=get_stats_P(x, y, metadata)
#MUC2 + alphadiv
x_label= "Observed features"
y_label= "MUC2"
x <- metadata$Alfa_Observed
y <- metadata$DeltaCq_MUC2
cec_MUC2_Alfa_Observed= plot_regression(x, y, metadata, x_label, y_label)
cec_MUC2_Alfa_Observed_R=get_stats_R(x, y, metadata)
cec_MUC2_Alfa_Observed_P=get_stats_P(x, y, metadata)
x_label= "Shannon diversity"
y_label= "MUC2"
x <- metadata$Alfa_Shannon
y <- metadata$DeltaCq_MUC2
cec_MUC2_Alfa_Shannon= plot_regression(x, y, metadata, x_label, y_label)
cec_MUC2_Alfa_Shannon_R=get_stats_R(x, y, metadata)
cec_MUC2_Alfa_Shannon_P=get_stats_P(x, y, metadata)
gird_cecum=ggarrange(cec_IL10_Alfa_Observed, cec_IL10_Alfa_Shannon,
cec_IL1B_Alfa_Observed, cec_IL1B_Alfa_Shannon,
cec_MUC2_Alfa_Observed,cec_MUC2_Alfa_Shannon,
ncol = 2, nrow = 3)
gird_cecum
```
### TABLE 6: CECUM - Statistics for the previous regressions {data-width=500}
```{r aplhadiv + ge cecum tab, include=TRUE, echo=FALSE, warning=FALSE}
tab <- matrix(c(cec_IL10_Alfa_Observed_R, cec_IL10_Alfa_Observed_P,
cec_IL10_Alfa_Shannon_R, cec_IL10_Alfa_Shannon_P,
cec_IL1B_Alfa_Observed_R, cec_IL1B_Alfa_Observed_P,
cec_IL1B_Alfa_Shannon_R, cec_IL1B_Alfa_Shannon_P,
cec_MUC2_Alfa_Observed_R, cec_MUC2_Alfa_Observed_P,
cec_MUC2_Alfa_Shannon_R,cec_MUC2_Alfa_Shannon_P), ncol=2, byrow=TRUE)
colnames(tab) <- c('R2','Pval')
rownames(tab) <- c('IL10_Observed','IL10_Shannon',
'IL1B_Observed', 'IL1B_Shannon',
'MUC2_Observed', 'MUC2_Shannon')
table_cecum_gene_aplhadiv <- as.table(tab)
kbl(table_cecum_gene_aplhadiv, row.names = TRUE) %>% kable_styling(position= "left", full_width = T)
```
Row
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### FIGURE 22: Alpha diversity vs Gene expression - Ileum {data-width=500}
```{r alphadiv + ge ileum, include=TRUE, echo=FALSE, warning=FALSE}
#Grid 2: ILEUM
metadata= metadata_ileum
#IL10 + alphadiv
x_label= "Observed features"
y_label= "IL10"
x <- metadata$Alfa_Observed
y <- metadata$ratio1LOG
ile_IL10_Alfa_Observed= plot_regression(x, y, metadata, x_label, y_label)
ile_IL10_Alfa_Observed_R=get_stats_R(x, y, metadata)
ile_IL10_Alfa_Observed_P=get_stats_P(x, y, metadata)
x_label= "Shannon diversity"
y_label= "IL10"
x <- metadata$Alfa_Shannon
y <- metadata$DeltaCq_IL10
ile_IL10_Alfa_Shannon= plot_regression(x, y, metadata, x_label, y_label)
ile_IL10_Alfa_Shannon_R=get_stats_R(x, y, metadata)
ile_IL10_Alfa_Shannon_P=get_stats_P(x, y, metadata)
#IL1B + alphadiv
x_label= "Observed features"
y_label= "IL1B"
x <- metadata$Alfa_Observed
y <- metadata$DeltaCq_IL1B
ile_IL1B_Alfa_Observed= plot_regression(x, y, metadata, x_label, y_label)
ile_IL1B_Alfa_Observed_R=get_stats_R(x, y, metadata)
ile_IL1B_Alfa_Observed_P=get_stats_P(x, y, metadata)
x_label= "Shannon diversity"
y_label= "IL1B"
x <- metadata$Alfa_Shannon
y <- metadata$DeltaCq_IL1B
ile_IL1B_Alfa_Shannon= plot_regression(x, y, metadata, x_label, y_label)
ile_IL1B_Alfa_Shannon_R=get_stats_R(x, y, metadata)
ile_IL1B_Alfa_Shannon_P=get_stats_P(x, y, metadata)
#MUC2 + alphadiv
x_label= "Observed features"
y_label= "MUC2"
x <- metadata$Alfa_Observed
y <- metadata$DeltaCq_MUC2
ile_MUC2_Alfa_Observed= plot_regression(x, y, metadata, x_label, y_label)
ile_MUC2_Alfa_Observed_R=get_stats_R(x, y, metadata)
ile_MUC2_Alfa_Observed_P=get_stats_P(x, y, metadata)
x_label= "Shannon diversity"
y_label= "MUC2"
x <- metadata$Alfa_Shannon
y <- metadata$DeltaCq_MUC2
ile_MUC2_Alfa_Shannon= plot_regression(x, y, metadata, x_label, y_label)
ile_MUC2_Alfa_Shannon_R=get_stats_R(x, y, metadata)
ile_MUC2_Alfa_Shannon_P=get_stats_P(x, y, metadata)
gird_ileum=ggarrange(ile_IL10_Alfa_Observed, ile_IL10_Alfa_Shannon,
ile_IL1B_Alfa_Observed, ile_IL1B_Alfa_Shannon,
ile_MUC2_Alfa_Observed,ile_MUC2_Alfa_Shannon,
ncol = 2, nrow = 3)
gird_ileum
```
### TABLE 7: ILEUM - Statistics for the previous regressions {data-width=500}
```{r alphadiv + ge ileum tab, include=TRUE, echo=FALSE, warning=FALSE}
tab <- matrix(c(ile_IL10_Alfa_Observed_R, ile_IL10_Alfa_Observed_P,
ile_IL10_Alfa_Shannon_R, ile_IL10_Alfa_Shannon_P,
ile_IL1B_Alfa_Observed_R, ile_IL1B_Alfa_Observed_P,
ile_IL1B_Alfa_Shannon_R, ile_IL1B_Alfa_Shannon_P,
ile_MUC2_Alfa_Observed_R, ile_MUC2_Alfa_Observed_P,
ile_MUC2_Alfa_Shannon_R,ile_MUC2_Alfa_Shannon_P), ncol=2, byrow=TRUE)
colnames(tab) <- c('R2','Pval')
rownames(tab) <- c('IL10_Observed','IL10_Shannon',
'IL1B_Observed', 'IL1B_Shannon',
'MUC2_Observed', 'MUC2_Shannon')
table_ileum_gene_aplhadiv <- as.table(tab)
kbl(table_ileum_gene_aplhadiv, row.names = TRUE) %>% kable_styling(position= "left", full_width = T)
```
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**Linear regressions between microbiome ratios and gene expression**
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### FIGURE 23: Ratios vs Gene Expression - Cecum {data-width=500}
```{r ratios + ge cecum, include=TRUE, echo=FALSE, warning=FALSE}
#Grid 1: CECUM
metadata= metadata_cecum
#IL10 + Ratios
x_label= "Ratio1"
y_label= "IL10"
x <- metadata$ratio1LOG
y <- metadata$DeltaCq_IL10
cec_IL10_Ratio1= plot_regression(x, y, metadata, x_label, y_label)
cec_IL10_Ratio1_R=get_stats_R(x, y, metadata)
cec_IL10_Ratio1_P=get_stats_P(x, y, metadata)
x_label= "Ratio2"
y_label= "IL10"
x <- metadata$ratio2LOG
y <- metadata$DeltaCq_IL10
cec_IL10_Ratio2= plot_regression(x, y, metadata, x_label, y_label)
cec_IL10_Ratio2_R=get_stats_R(x, y, metadata)
cec_IL10_Ratio2_P=get_stats_P(x, y, metadata)
x_label= "Ratio3"
y_label= "IL10"
x <- metadata$ratio3LOG
y <- metadata$DeltaCq_IL10
cec_IL10_Ratio3= plot_regression(x, y, metadata, x_label, y_label)
cec_IL10_Ratio3_R=get_stats_R(x, y, metadata)
cec_IL10_Ratio3_P=get_stats_P(x, y, metadata)
#IL1B + Ratios
x_label= "Ratio1"
y_label= "IL1B"
x <- metadata$ratio1LOG
y <- metadata$DeltaCq_IL1B
cec_IL1B_Ratio1= plot_regression(x, y, metadata, x_label, y_label)
cec_IL1B_Ratio1_R=get_stats_R(x, y, metadata)
cec_IL1B_Ratio1_P=get_stats_P(x, y, metadata)
x_label= "Ratio2"
y_label= "IL1B"
x <- metadata$ratio2LOG
y <- metadata$DeltaCq_IL1B
cec_IL1B_Ratio2= plot_regression(x, y, metadata, x_label, y_label)
cec_IL1B_Ratio2_R=get_stats_R(x, y, metadata)
cec_IL1B_Ratio2_P=get_stats_P(x, y, metadata)
x_label= "Ratio3"
y_label= "IL1B"
x <- metadata$ratio3LOG
y <- metadata$DeltaCq_IL1B
cec_IL1B_Ratio3= plot_regression(x, y, metadata, x_label, y_label)
cec_IL1B_Ratio3_R=get_stats_R(x, y, metadata)
cec_IL1B_Ratio3_P=get_stats_P(x, y, metadata)
#MUC2 + Ratios
x_label= "Ratio1"
y_label= "MUC2"
x <- metadata$ratio1LOG
y <- metadata$DeltaCq_MUC2
cec_MUC2_Ratio1= plot_regression(x, y, metadata, x_label, y_label)
cec_MUC2_Ratio1_R=get_stats_R(x, y, metadata)
cec_MUC2_Ratio1_P=get_stats_P(x, y, metadata)
x_label= "Ratio2"
y_label= "MUC2"
x <- metadata$ratio2LOG
y <- metadata$DeltaCq_MUC2
cec_MUC2_Ratio2= plot_regression(x, y, metadata, x_label, y_label)
cec_MUC2_Ratio2_R=get_stats_R(x, y, metadata)
cec_MUC2_Ratio2_P=get_stats_P(x, y, metadata)
x_label= "Ratio3"
y_label= "MUC2"
x <- metadata$ratio3LOG
y <- metadata$DeltaCq_MUC2
cec_MUC2_Ratio3= plot_regression(x, y, metadata, x_label, y_label)
cec_MUC2_Ratio3_R=get_stats_R(x, y, metadata)
cec_MUC2_Ratio3_P=get_stats_P(x, y, metadata)
gird_cecum=ggarrange(cec_IL10_Ratio1, cec_IL10_Ratio2, cec_IL10_Ratio3,
cec_IL1B_Ratio1,cec_IL1B_Ratio2, cec_IL1B_Ratio3,
cec_MUC2_Ratio1, cec_MUC2_Ratio2, cec_MUC2_Ratio3,
ncol = 3, nrow = 3)
gird_cecum
```
### TABLE 8: CECUM - Statistics for the previous regressions {data-width=500}
```{r ratios + ge cecum tabb, include=TRUE, echo=FALSE, warning=FALSE}
tab <- matrix(c(cec_IL10_Ratio1_R, cec_IL10_Ratio1_P,
cec_IL10_Ratio2_R, cec_IL10_Ratio2_P,
cec_IL10_Ratio3_R, cec_IL10_Ratio3_P,
cec_IL1B_Ratio1_R, cec_IL1B_Ratio1_P,
cec_IL1B_Ratio2_R, cec_IL1B_Ratio2_P,
cec_IL1B_Ratio3_R, cec_IL1B_Ratio3_P,
cec_MUC2_Ratio1_R, cec_MUC2_Ratio1_P,
cec_MUC2_Ratio2_R, cec_MUC2_Ratio2_P,
cec_MUC2_Ratio3_R, cec_MUC2_Ratio3_P), ncol=2, byrow=TRUE)
colnames(tab) <- c('R2','Pval')
rownames(tab) <- c('IL10_Ratio1','IL10_Ratio2', 'IL10_Ratio3',
'IL1B_Ratio1', 'IL1B_Ratio2', 'IL1B_Ratio3',
'MUC2_Ratio1', 'MUC2_Ratio2', 'MUC2_Ratio3')
table_cecum_gene_ratios<- as.table(tab)
kbl(table_cecum_gene_ratios, row.names = TRUE) %>% kable_styling(position= "left", full_width = T)
```
Row
-----------------------------------------------------------------------
### FIGURE 24: Ratios vs Gene Expression - Ileum {data-width=500}
```{r ratios + ge ileum, include=TRUE, echo=FALSE, warning=FALSE}
#Grid 2: ILEUM
metadata= metadata_ileum
#IL10 + Ratios
x_label= "Ratio1"
y_label= "IL10"
x <- metadata$ratio1LOG
y <- metadata$DeltaCq_IL10
ile_IL10_Ratio1= plot_regression(x, y, metadata, x_label, y_label)
ile_IL10_Ratio1_R=get_stats_R(x, y, metadata)
ile_IL10_Ratio1_P=get_stats_P(x, y, metadata)
x_label= "Ratio2"
y_label= "IL10"
x <- metadata$ratio2LOG
y <- metadata$DeltaCq_IL10
ile_IL10_Ratio2= plot_regression(x, y, metadata, x_label, y_label)
ile_IL10_Ratio2_R=get_stats_R(x, y, metadata)
ile_IL10_Ratio2_P=get_stats_P(x, y, metadata)
x_label= "Ratio3"
y_label= "IL10"
x <- metadata$ratio3LOG
y <- metadata$DeltaCq_IL10
ile_IL10_Ratio3= plot_regression(x, y, metadata, x_label, y_label)
ile_IL10_Ratio3_R=get_stats_R(x, y, metadata)
ile_IL10_Ratio3_P=get_stats_P(x, y, metadata)
#IL1B + Ratios
x_label= "Ratio1"
y_label= "IL1B"
x <- metadata$ratio1LOG
y <- metadata$DeltaCq_IL1B
ile_IL1B_Ratio1= plot_regression(x, y, metadata, x_label, y_label)
ile_IL1B_Ratio1_R=get_stats_R(x, y, metadata)
ile_IL1B_Ratio1_P=get_stats_P(x, y, metadata)
x_label= "Ratio2"
y_label= "IL1B"
x <- metadata$ratio2LOG
y <- metadata$DeltaCq_IL1B
ile_IL1B_Ratio2= plot_regression(x, y, metadata, x_label, y_label)
ile_IL1B_Ratio2_R=get_stats_R(x, y, metadata)
ile_IL1B_Ratio2_P=get_stats_P(x, y, metadata)
x_label= "Ratio3"
y_label= "IL1B"
x <- metadata$ratio3LOG
y <- metadata$DeltaCq_IL1B
ile_IL1B_Ratio3= plot_regression(x, y, metadata, x_label, y_label)
ile_IL1B_Ratio3_R=get_stats_R(x, y, metadata)
ile_IL1B_Ratio3_P=get_stats_P(x, y, metadata)
#MUC2 + Ratios
x_label= "Ratio1"
y_label= "MUC2"
x <- metadata$ratio1LOG
y <- metadata$DeltaCq_MUC2
ile_MUC2_Ratio1= plot_regression(x, y, metadata, x_label, y_label)
ile_MUC2_Ratio1_R=get_stats_R(x, y, metadata)
ile_MUC2_Ratio1_P=get_stats_P(x, y, metadata)
x_label= "Ratio2"
y_label= "MUC2"
x <- metadata$ratio2LOG
y <- metadata$DeltaCq_MUC2
ile_MUC2_Ratio2= plot_regression(x, y, metadata, x_label, y_label)
ile_MUC2_Ratio2_R=get_stats_R(x, y, metadata)
ile_MUC2_Ratio2_P=get_stats_P(x, y, metadata)
x_label= "Ratio3"
y_label= "MUC2"
x <- metadata$ratio3LOG
y <- metadata$DeltaCq_MUC2
ile_MUC2_Ratio3= plot_regression(x, y, metadata, x_label, y_label)
ile_MUC2_Ratio3_R=get_stats_R(x, y, metadata)
ile_MUC2_Ratio3_P=get_stats_P(x, y, metadata)
gird_ileum=ggarrange(ile_IL10_Ratio1, ile_IL10_Ratio2, ile_IL10_Ratio3,
ile_IL1B_Ratio1,ile_IL1B_Ratio2, ile_IL1B_Ratio3,
ile_MUC2_Ratio1, ile_MUC2_Ratio2, ile_MUC2_Ratio3,
ncol = 3, nrow = 3)
gird_ileum
```
### TABLE 9: ILEUM - Statistics for the previous regressions {data-width=500}
```{r ratios + ge ileum tabb, include=TRUE, echo=FALSE, warning=FALSE}
tab <- matrix(c(ile_IL10_Ratio1_R, ile_IL10_Ratio1_P,
ile_IL10_Ratio2_R, ile_IL10_Ratio2_P,
ile_IL10_Ratio3_R, ile_IL10_Ratio3_P,
ile_IL1B_Ratio1_R, ile_IL1B_Ratio1_P,
ile_IL1B_Ratio2_R, ile_IL1B_Ratio2_P,
ile_IL1B_Ratio3_R, ile_IL1B_Ratio3_P,
ile_MUC2_Ratio1_R, ile_MUC2_Ratio1_P,
ile_MUC2_Ratio2_R, ile_MUC2_Ratio2_P,
ile_MUC2_Ratio3_R, ile_MUC2_Ratio3_P), ncol=2, byrow=TRUE)
colnames(tab) <- c('R2','Pval')
rownames(tab) <- c('IL10_Ratio1','IL10_Ratio2', 'IL10_Ratio3',
'IL1B_Ratio1', 'IL1B_Ratio2', 'IL1B_Ratio3',
'MUC2_Ratio1', 'MUC2_Ratio2', 'MUC2_Ratio3')
table_ileum_gene_ratios<- as.table(tab)
kbl(table_ileum_gene_ratios, row.names = TRUE) %>% kable_styling(position= "left", full_width = T)
```
Row {data-height=50}
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Row {data-height=30}
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**Diversity indexes by Histopathology score**
```{r Indexes histo organize, include=FALSE}
#####Open ratio data#####
alpha_index_data <- subset( complete_sample_table, select = c("SampleID", "Alfa_Shannon","Alfa_Observed" ))
#merge dataframes ratios and histo
alpha_index_data <- alpha_index_data %>%
gather(Index, Value, -SampleID)
boxplot_data <- merge(alpha_index_data, histo_data_melted , by = "SampleID")
boxplot_data$SampleLocation <- str_sub(boxplot_data$SampleID, -3,-3)
boxplot_data$Category <- str_replace(boxplot_data$Category, "LEVEL", "")
#Separate data for C and I
boxplot_data_cecum <- boxplot_data[boxplot_data$SampleLocation =="C",]
boxplot_data_ileum <- boxplot_data[boxplot_data$SampleLocation =="I",]
```
Row
-----------------------------------------------------------------------
### FIGURE 25A: Histo vs Alpha diversity - Cecum {data-width=500}
```{r Indexes histo plots cec, include=TRUE, echo=FALSE}
#cecum plot
bp <- ggplot(boxplot_data_cecum, aes(x=Score, y=Value)) +
geom_boxplot(aes(fill=Category))
bp_cecum <- bp + facet_grid(Index~ Category, scales = "free")+
xlab("") + ylab("Ratio Value") +
theme(legend.position = "none") +
theme(text=element_text(size=10))
bp_cecum
```
### FIGURE 25B: Histo vs Alpha diversity - Ileum s{data-width=500}
```{r Indexes histo plots ile, include=TRUE, echo=FALSE}
#Ileum plot
bp <- ggplot(boxplot_data_ileum, aes(x=Score, y=Value)) +
geom_boxplot(aes(fill=Category))
bp_ileum <- bp + facet_grid(Index~ Category, scales = "free")+
xlab("") + ylab("Ratio Value") +
theme(legend.position = "none") +
theme(text=element_text(size=10))
bp_ileum
```
Row {data-height=50}
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Row {data-height=30}
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**Microbiome Ratios by Histopathology score **
```{r ratios histo organize, include=FALSE}
#####Open ratio data#####
ratios_data <- subset( complete_sample_table, select = c("SampleID", "ratio1LOG", "ratio2LOG", "ratio3LOG" ))
ratios_data_melted <- ratios_data %>%
gather(Ratio, Value, -SampleID)
boxplot_data <- merge(ratios_data_melted, histo_data_melted , by = "SampleID")
boxplot_data$SampleLocation <- str_sub(boxplot_data$SampleID, -3,-3)
boxplot_data$Category <- str_replace(boxplot_data$Category, "LEVEL", "")
#Separate data for C and I
boxplot_data_cecum <- boxplot_data[boxplot_data$SampleLocation =="C",]
boxplot_data_ileum <- boxplot_data[boxplot_data$SampleLocation =="I",]
```
Row
-----------------------------------------------------------------------
### FIGURE 26A: Histo vs Ratios - Cecum {data-width=500}
```{r ratios histo plots cec, include=TRUE, echo=FALSE}
#cecum plot
bp <- ggplot(boxplot_data_cecum, aes(x=Score, y=Value)) +
geom_boxplot(aes(fill=Category))
bp_cecum <- bp + facet_grid(Ratio~ Category)+
xlab("") + ylab("Ratio Value") +
theme(legend.position = "none") +
theme(text=element_text(size=10))
bp_cecum
```
### FIGURE 26B: Histo vs Ratios - Ileum{data-width=500}
```{r ratios histo plots ile, include=TRUE, echo=FALSE}
#Ileum plot
bp <- ggplot(boxplot_data_ileum, aes(x=Score, y=Value)) +
geom_boxplot(aes(fill=Category))
bp_ileum <- bp + facet_grid(Ratio~ Category)+
xlab("") + ylab("Ratio Value") +
theme(legend.position = "none") +
theme(text=element_text(size=10))
bp_ileum
```
